Here is an answer for any number $n$ of variables.
For any partition
$\lambda$, we let $s_{\lambda}$ denote the Schur
function
corresponding to $\lambda$. Let $\delta$ be the partition $\left(
n-1,n-2,\ldots,1\right) $ of $n\left( n-1\right) /2$. Then,
\begin{equation}
\prod_{1\leq i<j\leq n}\left( x_{i}+x_{j}\right)
= s_{\delta}\left( x_{1},x_{2},\ldots,x_{n}\right) .
\label{1}
\tag{1}
\end{equation}
This well-known formula (which, I think, is due to Jacobi) can easily be
derived from the definition of Schur polynomials through alternants. Let me explain:
For every $n$-tuple $\alpha=\left( \alpha_{1},\alpha_{2},\ldots,\alpha
_{n}\right) \in\mathbb{N}^{n}$ of nonnegative integers, we define the
alternant $a_{\alpha}$ to be the polynomial
\begin{equation}
\sum_{\sigma\in S_{n}}\left( -1\right) ^{\sigma}x_{1}^{\alpha_{\sigma\left( 1\right) }}x_{2}^{\alpha_{\sigma\left( 2\right) }}\cdots x_{n}^{\alpha_{\sigma\left( n\right) }}
= \det\left( \left( x_i^{\alpha_j}\right) _{1\leq i\leq n,\ 1\leq
j\leq n}\right)
\end{equation}
in $\mathbb{Z}\left[ x_{1},x_{2},\ldots,x_{n}\right] $. If $\alpha=\left(
\alpha_{1},\alpha_{2},\ldots,\alpha_{n}\right) $ and $\beta=\left( \beta
_{1},\beta_{2},\ldots,\beta_{n}\right) $ are two $n$-tuples in $\mathbb{N}
^{n}$, then the $n$-tuple $\alpha+\beta$ is defined to be $\left( \alpha
_{1}+\beta_{1},\alpha_{2}+\beta_{2},\ldots,\alpha_{n}+\beta_{n}\right) $.
Any partition $\lambda=\left( \lambda_{1},\lambda_{2},\ldots,\lambda
_{k}\right) $ of length $k\leq n$ will be identified with the $n$-tuple
$\left( \lambda_{1},\lambda_{2},\ldots,\lambda_{k},\underbrace{0,0,\ldots
,0}_{n-k\text{ times}}\right) \in\mathbb{N}^{n}$. Notice that $\delta$ is
thus identified with the $n$-tuple $\left( n-1,n-2,\ldots,1,0\right) $.
Then, the "alternant formula" for the Schur polynomial $s_{\lambda}\left(
x_{1},x_{2},\ldots,x_{n}\right) $ says that
\begin{equation}
s_{\lambda}\left( x_{1},x_{2},\ldots,x_{n}\right) =\dfrac{a_{\lambda+\delta
}}{a_{\delta}}
\label{2}
\tag{2}
\end{equation}
whenever $\lambda$ is a partition of length $\leq n$. This is the historically
first definition of Schur polynomials, long before the modern definitions via
Young tableaux or the Jacobi-Trudi formulas were discovered; its main
disadvantages are that it only defines $s_{\lambda}$ in finitely many
variables and that it requires proof that $\dfrac{a_{\lambda+\delta}
}{a_{\delta}}$ is indeed a polynomial. But this is fine for us.
(For a proof of the fact that this definition of $s_\lambda$ is equivalent
to the modern combinatorial definition, you can consult Corollary 2.6.6 in Darij Grinberg and Victor Reiner, Hopf algebras in combinatorics, arXiv:1409.8356v5. In the same chapter you will find an exercise proving the Jacobi-Trudi identity, which is yet another popular definition of the Schur polynomials.)
We can now prove \eqref{1}. Indeed, $\delta=\left( n-1,n-2,\ldots,0\right)
\in\mathbb{N}^{n}$, so that the definition of $a_{\delta}$ yields
\begin{equation}
a_{\delta+\delta}=\det\left( \left( x_{i}^{n-j}\right) _{1\leq i\leq
n,\ 1\leq j\leq n}\right) =\prod_{1\leq i<j\leq n}\left( x_{i}-x_{j}\right)
\label{3}
\tag{3}
\end{equation}
(by the Vandermonde determinant formula). But $\delta+\delta=\left( 2\left(
n-1\right) ,2\left( n-2\right) ,\ldots,2\cdot0\right) $, so that
\begin{align}
a_{\delta+\delta} & =\det\left( \left( x_{i}^{2\left( n-j\right)
}\right) _{1\leq i\leq n,\ 1\leq j\leq n}\right) =\det\left( \left(
\left( x_{i}^{2}\right) ^{n-j}\right) _{1\leq i\leq n,\ 1\leq j\leq
n}\right) \nonumber\\
& =\prod_{1\leq i<j\leq n}\left( x_{i}^{2}-x_{j}^{2}\right)
\label{4}
\tag{4}
\end{align}
(again by the Vandermonde determinant formula). Dividing \eqref{4} by
\eqref{3}, we obtain
\begin{equation}
\dfrac{a_{\delta+\delta}}{a_{\delta}}=\dfrac{\prod_{1\leq i<j\leq n}\left(
x_{i}^{2}-x_{j}^{2}\right) }{\prod_{1\leq i<j\leq n}\left( x_{i}
-x_{j}\right) }=\prod_{1\leq i<j\leq n}\underbrace{\dfrac{x_{i}^{2}-x_{j}
^{2}}{x_{i}-x_{j}}}_{=x_{i}+x_{j}}=\prod_{1\leq i<j\leq n}\left( x_{i}
+x_{j}\right) .
\end{equation}
But applying \eqref{2} to $\lambda=\delta$, we obtain $s_{\delta}\left(
x_{1},x_{2},\ldots,x_{n}\right) =\dfrac{a_{\delta+\delta}}{a_{\delta}}
=\prod_{1\leq i<j\leq n}\left( x_{i}+x_{j}\right) $. Thus, \eqref{1} is proven.
Finally, we can transform \eqref{1} into an explicit (if you allow
determinants) formula for $\prod_{1\leq i<j\leq n}\left( x_{i}+x_{j}\right)
$ in terms of the elementary symmetric polynomials. To that aim, we let
$e_{k}\left( \overrightarrow{x}\right) $ denote the $k$-th elementary
symmetric polynomial in $x_{1},x_{2},\ldots,x_{n}$. Notice that $e_{k}\left(
\overrightarrow{x}\right) =0$ whenever $k<0$ and also whenever $k>n$.
Recall that one of the two Jacobi-Trudi formulas says that if $\lambda$ is a
partition of length $\leq n$, then
\begin{equation}
s_{\lambda^{t}}\left( x_{1},x_{2},\ldots,x_{n}\right) =\det\left( \left(
e_{\lambda_{i}-i+j}\left( \overrightarrow{x}\right) \right) _{1\leq i\leq
n,\ 1\leq j\leq n}\right) ,
\end{equation}
where $\lambda^{t}$ denotes the conjugate partition of $\lambda$ (this is the
partition whose $i$-th entry is the number of entries of $\lambda$ that are
$\geq i$, for each $i\in\left\{ 1,2,3,\ldots\right\} $). Applying this to
$\lambda=\delta$, we obtain
\begin{align*}
s_{\delta^{t}}\left( x_{1},x_{2},\ldots,x_{n}\right) & =\det\left(
\left( e_{\left( n-i\right) -i+j}\left( \overrightarrow{x}\right)
\right) _{1\leq i\leq n,\ 1\leq j\leq n}\right) \\
& =\det\left( \left( e_{n-2i+j}\left( \overrightarrow{x}\right) \right)
_{1\leq i\leq n,\ 1\leq j\leq n}\right) \\
& =\det\left(
\begin{array}
[c]{cccc}
e_{n-1}\left( \overrightarrow{x}\right) & e_{n}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-2}\left( \overrightarrow{x}\right) \\
e_{n-3}\left( \overrightarrow{x}\right) & e_{n-2}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-4}\left( \overrightarrow{x}\right) \\
\vdots & \vdots & \ddots & \vdots\\
e_{-n+1}\left( \overrightarrow{x}\right) & e_{-n+2}\left(
\overrightarrow{x}\right) & \cdots & e_{0}\left( \overrightarrow{x}\right)
\end{array}
\right) .
\end{align*}
Since $\delta^{t}=\delta$ (this is easy to check), this simplifies to
\begin{equation}
s_{\delta}\left( x_{1},x_{2},\ldots,x_{n}\right) =\det\left(
\begin{array}
[c]{cccc}
e_{n-1}\left( \overrightarrow{x}\right) & e_{n}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-2}\left( \overrightarrow{x}\right) \\
e_{n-3}\left( \overrightarrow{x}\right) & e_{n-2}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-4}\left( \overrightarrow{x}\right) \\
\vdots & \vdots & \ddots & \vdots\\
e_{-n+1}\left( \overrightarrow{x}\right) & e_{-n+2}\left(
\overrightarrow{x}\right) & \cdots & e_{0}\left( \overrightarrow{x}\right)
\end{array}
\right) .
\end{equation}
Thus, \eqref{1} rewrites as
\begin{equation}
\prod_{1\leq i<j\leq n}\left( x_{i}+x_{j}\right)
=\det\left(
\begin{array}
[c]{cccc}
e_{n-1}\left( \overrightarrow{x}\right) & e_{n}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-2}\left( \overrightarrow{x}\right) \\
e_{n-3}\left( \overrightarrow{x}\right) & e_{n-2}\left( \overrightarrow{x}
\right) & \cdots & e_{2n-4}\left( \overrightarrow{x}\right) \\
\vdots & \vdots & \ddots & \vdots\\
e_{-n+1}\left( \overrightarrow{x}\right) & e_{-n+2}\left(
\overrightarrow{x}\right) & \cdots & e_{0}\left( \overrightarrow{x}\right)
\end{array}
\right) .
\end{equation}
Note that the matrix on the right hand side is not generally upper-triangular,
but it has a lot of zeroes (more or less the whole lower half of its
southwestern triangle consists of zeroes), so its determinant is a bit easier
to compute than a general $n\times n$ determinant. But I don't think there is
a more explicit formula.
The implementation in Sage is for symmetric functions, rather than symmetric polynomials. A symmetric function is a formal power series in a countable number of variables.
Symmetric functions are indexed by partitions, but the length of these partitions is not bounded. Consequently, ending zeros are ignored. For instance,
$$s_{(1,0,0,...)} = s_{(1)} = x_1 + x_2 + x_3 + \cdots.$$
In Sage, this is s[1].
If you want to work exclusively with polynomials in, say, $n$ variables, you could work with symmetric functions and ignore any $s_\lambda$ indexed by a partition of length greater or equal to $n$ (the length being the number of nonzero entries of the partition). Alternatively, one can see the explicit polynomial with the command f.expand(n). For example,
S = SymmetricFunctions(QQ)
s = S.s()
s[1].expand(2)
prints
x0+x1
Best Answer
The second expression gives $s_\lambda(x)=\sum_{w\in S_2}\dfrac{x_1^3x_2}{1-x_2/x_1}$, so $s_\lambda(x)=\dfrac{x_1^3x_2}{1-x_2/x_1}+\dfrac{x_2^3x_1}{1-x_1/x_2}=\dfrac{x_1^4x_2-x_1x_2^4}{x_1-x_2}$ which is what you got from the first formula.
To see the equivalence of the two definitions: Note that $$ \sum_{\sigma \in S_n} \sigma \bigg(\dfrac{x^\lambda}{\prod_{i<j} 1- x_j/x_i}\bigg)=\sum_{\sigma \in S_n} \sigma \bigg(\dfrac{x^\lambda x_1^{n-1}x_2^{n-2}...x_{n-1}}{\prod_{i<j} (x_i- x_j)} \bigg)$$.
Now $\Delta= \prod_{i<j} (x_i- x_j)$ is skew-symmetric, i.e, $w \Delta = \epsilon(w) \Delta$ for $w \in S_n$. Therefore, the right hand side of the above expression simplifies to $$ \Delta^{-1} \sum_{\sigma \in S_n} \epsilon (\sigma) (x^{\lambda+\rho}) $$ where $\rho=(n-1,n-2,...,1,0)$.
Now I leave you to check that the sum in the above expression is the determinant of the matrix $(x_j^{\lambda_i+n-i})$.