I think the nice added conjecture goes to the core of the problem, nevertheless it has to be modified because it doesn't hold as it is.
Consider for instance an entire function
$f(x):=\sum_{n=0}^\infty a_n x^n$ with
$$|a_n|=3^{-n^2}.$$
then $f$ is unbounded on $\mathbb{R}$ since we have, for all $a\in\mathbb{N}:$
$$\left|\ f(3^{2a})\right| \geq 3^{a^2} -\sum_{n\neq a} 3^{n(2a-n)}\geq 3^{a^2}\left( 1-2\sum_{k>0 }3^{-k^2}\right)\ge \frac{3^{a^2}}{4}.$$
I sketch the arguments for $C(x)$, the arguments for $L(x)$ are essentially the same.
The specific form of the sum suggests probabilistic arguments.
Let $X_x$ be a $\mathrm{Poiss}(x^2)$-distributed random variable and note that
$$C(x)\,e^{-x^2}=\mathbb{E}\frac{x}{\sqrt{X_x}}\,1_{\{X_x\geq 1\}}$$
It is known that $\frac{X_x -x^2}{x}\longrightarrow N(0,1)$ (standard normal) in distribution as $x\longrightarrow \infty$ and that
that all moments $\mathbb{E}\left(\frac{X_x-x^2}{x}\right)^k$ of $X_x$ converge to the corresponding moments of $N(0,1)$, so that (for large $x$) $X_x$ is concentrated around $x^2$, with deviations of order $x$.
To use that information split $\{X_x\geq 1\}$ on the rhs into (say) the parts $1\leq X_x <\tfrac{1}{2}x^2$, $X_x-x^2 >\tfrac{1}{2} x^{2}$ and
$|X_x-x^2| \le \tfrac{1}{2}x^{2}$.
By routine arguments the integrals over the first two parts are asymptotically exponentially small.
For the remaining part write
\begin{align*}
\mathbb{E}\frac{x}{\sqrt{X_x}}\,1_{\{|X_x-x^2|\leq \tfrac{1}{2}x^2\}}
&=\mathbb{E}\frac{x}{\sqrt{x^2+(X_x-x^2)}}\,1_{\{|X_x-x^2|\leq \tfrac{1}{2} x^2\}}\\
&=\mathbb{E}\frac{1}{\sqrt{1 +\frac{1}{x^2}(X_x-x^2)}}\,1_{\{|X_x-x^2|\leq \tfrac{1}{2} x^2\}}\\
&=\mathbb{E}\sum_{k=0}^\infty {-\frac{1}{2} \choose k} \frac{1}{x^{2k}}(X_x-x^2)^k
\,1_{\{|X_x-x^2|\leq \tfrac{1}{2}x^2\}}\\
%C(x)\,e^{-x^2}&= 1+\frac{3}{8}x^{-2} + \frac{65}{128}x^{-4} + \frac{1225}{1024}x^{-6} + \frac{1619583}{425984}x^{-8}+\mathcal{O}(x^{-10})\\
%L(x)\,x^2\,e^{-x^2}&= 1+\frac{15}{8}x^{-2} + \frac{665}{128}x^{-4}+\frac{19845}{1024}x^{-6}+\frac{37475823}{425984}x^{-8}+\mathcal{O}(x^{-10})
\end{align*}
Clearly the series may be integrated termwise, and completing the tails changes it only by asymptotically exponentially small terms. For $k\geq 2$ the central moment
$c_k(x):=\mathbb{E}\left(X_x-x^2\right)^k$ is a polynomial in $x^2$ of degree $\lfloor k/2\rfloor$.
Thus (after regrouping of terms) the formal series
$$\sum_{k=0}^\infty x^{-2k}{-\tfrac{1}{2} \choose k} c_k(x)$$
gives a full asymptotic expansion of $C(x)e^{-x^2}$. Evaluating the first eight terms gives
$$C(x)\,e^{-x^2}= 1+\frac{3}{8}x^{-2} + \frac{65}{128}x^{-4} + \frac{1225}{1024}x^{-6} + \frac{131691}{32768}x^{-8}+\mathcal{O}(x^{-10})$$
EDIT: I corrected the coefficient of $x^{-8}$. Thanks to Johannes Trost
for pointing out that I miscalculated. Similarly the coefficient of $x^{-8}$
in the asymptotic series for $L(x)$ given in the comment must be corrected.
Best Answer
Looks like the computers really spoiled us :)
GH gave a perfectly valid answer already but the cheapest way to prove positivity is to write $\int_0^1(1-t^n)\log(\frac 1t)^{-3/2}\,\frac{dt}t=c\sqrt n$ with some positive $c$ (just note that the integral converges and the integrand is positive, and make the change of variable $t^n\to t$). Hence $\int_0^1 (f(x)-f(xt))\log(\frac 1t)^{-3/2}\,\frac{dt}t=cxf(x)$. If $x$ is the largest zero of $f$ (which must be negative), then plugging it in, we get $0$ on the right and a negative number on the left, which is a clear contradiction. Thus, crossing the $x$-axis is impossible. Of course, there is nothing sacred about $1/2$. Any power between $0$ and $1$ works just as well.