This answer shows that all random variables yield simultaneous convergence over sets $(-\infty, x]$ for all $x\in \mathbb{R}$. However, we get simultaneous convergence over all sets in $\mathcal{B}(\mathbb{R})$ if and only if the random variable is discrete.
Let $(\Omega, \mathcal{F}, P)$ be a probability triple and let $\{X_i\}_{i=1}^{\infty}$ be i.i.d. random variables. Define $X=X_1$.
1) Suppose $X$ has any CDF $F_X(x)$. The Glivenko-Cantelli theorem implies that
$$ P\left[\lim_{n\rightarrow\infty}Q_n((-\infty, x])=F_X(x) \quad \forall x \in \mathbb{R}\right]=1$$
This positively answers the question "Can it converge strongly at least over some uncountable subset $S \subseteq \mathcal{B}(\mathbb{R})$?"
2) Suppose $X$ takes values in a finite or countably infinite set $\mathcal{X}$. We want to show
$$ P\left[\lim_{n\rightarrow\infty} Q_n(A)= P[X\in A] \quad \forall A \in \mathcal{B}(\mathbb{R})\right] = 1$$
It suffices to show
$$ P\left[\lim_{n\rightarrow\infty}Q_n(V) = P[X \in V] \quad \forall V \subseteq \mathcal{X}\right]=1$$
The law of large numbers implies that for any element $x \in \mathcal{X}$ we get:
$$ \lim_{n\rightarrow\infty}Q_n(\{x\}) = P[X=x] \quad \mbox{with prob 1}$$
Define the event
$$ B = \cap_{x \in \mathcal{X}} \left\{\lim_{n\rightarrow\infty}Q_n(\{x\})= P[X=x]\right\}$$
Since $\mathcal{X}$ is at most a countably infinite set we get $P[B]=1$. It suffices to show
$$ B\subseteq \left\{\lim_{n\rightarrow\infty}Q_n(V)= P[X \in V] \quad \forall V \subseteq \mathcal{X}\right\}$$
(In fact, the above inclusion will imply that $B$ is equal to the set on the right-hand-side, since the opposite inclusion clearly holds.) Suppose $B$ holds true. Fix $V \subseteq \mathcal{X}$. We get
$$ Q_n(V) = \sum_{x \in V} Q_n(\{x\})$$
Now, because $B$ holds true, we know $Q_n(\{x\})$ values are nonnegative and satisfy
$Q_n(\{x\}) \rightarrow P[X=x]$ for all $x \in V$. Also we get $Q_n(V)\leq 1$ for all $n$. So we can dominate the sum by 1 and we get by Lebesgue dominated convergence (applied deterministically to the summation via the counting measure):
$$ \lim_{n\rightarrow\infty} Q_n(V) = \sum_{x \in V} \lim_{n\rightarrow\infty} Q_n(\{x\}) = \sum_{x \in V} P[X=x] = P[X \in V]$$ $\Box$
3) This section shows that simultaneous convergence for all sets in $\mathcal{B}(\mathbb{R})$ (surely) fails if $X$ is not a discrete random variable.
Claim:
Let $\mathcal{X}$ be the set of all points $x \in \mathbb{R}$ for which $P[X=x]>0$ and suppose $P[X \in \mathcal{X}]<1$. Define the set
$$ B = \left\{\omega \in \Omega: \lim_{n\rightarrow\infty} Q_n(A) = P[X\in A] \quad \forall A \in \mathcal{B}(\mathbb{R})\right\}$$
Then $B$ is the empty set and so $P[B]=0$.
Proof:
We know $\mathcal{X}$ is either finite or countably infinite and so $\mathcal{X} \in \mathcal{B}(\mathbb{R})$. By definition of $\mathcal{X}$ we know that $P[X =v]=0$ for all $v \in \mathcal{X}^c$. Also,
$$P[X \in \mathcal{X}^c] =1-P[X\in \mathcal{X}]>0$$
Define $\theta = P[X \in \mathcal{X}^c]$ (so $\theta >0$).
Define $\mathcal{J}$ as the collection of all sets of the type $\mathcal{X} \cup C$ where $C$ is a finite or countably infinite subset of $\mathcal{X}^c$. Note that every set $J \in \mathcal{J}$ is in $\mathcal{B}(\mathbb{R})$. Letting $J=\mathcal{X} \cup C$ be a particular set in $\mathcal{J}$, we have
\begin{align}
P[X \in J] &= P[X \in \mathcal{X} \cup C] \\
&= P[X \in \mathcal{X}] + P[X \in C] \\
&= (1-\theta) + \sum_{v \in C}\underbrace{P[X=v]}_{0}\\
&=1-\theta \\
&<1
\end{align}
Now, for all $\omega \in \Omega$ and all $A\subseteq \mathbb{R}$ define:
$$Q_n^{\omega}(A) = \frac{1}{n}\sum_{i=1}^n I\{X_i(\omega) \in A\}$$
For a given outcome $\omega \in \Omega$ we can define the set $D(\omega)\subseteq \mathbb{R}$ by:
$$ D(\omega)= \mathcal{X} \cup \{x \in \mathcal{X}^c: x = X_n(\omega) \quad \mbox{for some $n \in \{1, 2, 3, ...\}$}\}$$
Then $D(\omega)$ is just the union of $\mathcal{X}$ with a finite or countably infinite number of elements of $\mathcal{X}^c$. Thus,
$$D(\omega) \in \mathcal{J} \quad \forall \omega \in \Omega$$
It is clear by the definition of $D(\omega)$ that
$$ I\{X_n(\omega) \in D(\omega)\} = 1 \quad \forall \omega \in \Omega, n \in \{1, 2, ,3 ...\}$$
and so
$$ Q_n^{\omega}(D(\omega)) = 1 \quad \forall \omega \in \Omega, n \in \{1, 2, ,3 ...\}$$
Thus
$$ \lim_{n\rightarrow\infty} Q_n^{\omega}(D(\omega)) = 1 \quad \forall \omega \in \Omega$$
Hence
$$ \left\{\omega \in \Omega : \cap_{J \in \mathcal{J}} \left\{\lim_{n\rightarrow\infty} Q_n^{\omega}(J) = \underbrace{P[X \in J]}_{1-\theta}\right\}\right\} =\phi $$
where $\phi$ is the empty set. This is because the above intersection over all sets in $\mathcal{J}$ includes the set $D(\omega)$, for which we know the limit is $1$ (not $1-\theta$). Since all sets in $\mathcal{J}$ are in $\mathcal{B}(\mathbb{R})$ it holds that
$$ \left\{\omega \in \Omega : \cap_{A \in \mathcal{B}(\mathbb{R})} \left\{ \lim_{n\rightarrow\infty} Q_n^{\omega}(A)= P[X\in A]\right\}\right\} = \phi$$ $\Box$
The following result is found in many books, in particular K L Chung's book:
If $F_n,F$ are distribution functions such that $F_n(t) \to F(t)$ on dense set of points $t$ and if $F$ is continuous then $F_n \to F$ uniformly.
In our case there is a null set outside which $F_n(t) \to F(t)$ for all $t$ rational. Hence the result follows.
Best Answer
The result is true.
Theorem 11.4.1 in Real Analysis and Probability by R.M.Dudley explains why the empirical measures converge almost surely for a Borel probability measure $\mu$ on a separable metric space $(S,d)$. I can provide more detail if you can't find this reference.