This is a good question about practical simulation, but it needs
a bit of a reinterpretation.  This is what I understand to be
the question(s):

> Q1. What is **meant** by saying that we are sampling finitely represented numbers (e.g. IEEE floating point) from a continuous distribution, given by a density function which is zero at some points?  For example, sampling from the uniform distribution over the closed interval $[0,1]$ or the open interval $(0,1)$.
>
> Q2: How is the intended sampling **achieved** in practice?

For starters, recall that you cannot exactly simulate from a
continuous distribution anyway, in a computer with bounded storage and
bounded time. This holds even if the density is nowhere zero, say, the
standard normal distribution: your simulated values will be in some
finite set, whether that is IEEE floating point, or something else.

So there are in fact two (related) distributions here: The underlying continuous distribution, and the discrete *sampling distribution* of
the values that you are really receiving.

Answer to Q1
============

The canonical example is simulating from the uniform distribution over
the open interval $(0,1)$, or the half-open interval $[0,1)$ or
$(0,1]$. Simulating from $\mathrm{Unif}(0,1]$, what you typically get
is an IEEE floating point number. If you take it at face value, it is
a rational number, from a particular finite subset of
$\mathbb{Q}$. Among other things this set includes integers of some
bounded size. Your next arithmetic operation might well be (say)
$1/x$, and it would be inconvenient if $x=0$ has positive probability.

Note that mathematically there is *no difference* in "the uniform over
$(0,1)$" and "the uniform over $[0,1]$". They specify the *same*
cumulative distribution function.  So there is no difference in
sampling from this or that (because they are the same thing).

What is **meant** is a difference in the **sampling distribution**,
not in the underlying continuous distribution.  I believe it is
usually meant, and desired, that the sampling should never return a
(floating) number $x_0$ where the underlying density is zero.

In particular, by saying "sample from the uniform over $(0,1)$", we
mean "sample from that distribution *so that the samples are in
the interval* $(0,1)$".  Similar for closed and half-open intervals.  This is
a convenient mathematical fiction, or abuse of notation.

Answer to Q2
============

Actual pseudorandom generators do take the endpoints into account.
For example, the Python [random][1] says it will return a "random
floating point number in the range 0.0 <= X < 1.0", and that's what it
does. The [implementation][2] is not that difficult: First it
generates a pseudorandom integer in range $0,\ldots,2^{53}-1 =
9007199254740991$. Then divides by $2^{53}$.  The precision has been
chosen such that the maximum possible value is a float strictly
smaller than $1.0$.  So it never returns $1.0$, not even with a small
probability

If you want to simulate from $\mathrm{Unif}(0,1)$ with both endpoints
impossible, there are two simple solutions. One is to reject and
resample if the undesired value happens (which is rare). The other is
to sample the integer from $1,\dots,2^{53}-1$ in the first place.

Endpoints are usually the main concern here, and they are easily dealt
with.  Another typical endpoint case is sampling a positive waiting
time from $\mathrm{Exp}(\lambda)$.

Much less common would be isolated zeros in the density, such as $f(x)
= (3/2)x^2$ for $-1 < x < 1$.  If you don't want $x=0$ to ever happen
in your simulation, the obvious easy solution is rejection and
resampling in that rare case.  There are other solutions that avoid
resampling: you could first sample from the strictly positive part,
then flip the sign with probability $0.5$.

Note: A [related question][3] about the implementation has been asked
and answered in Cross Validated.

  [1]: https://docs.python.org/3/library/random.html#random.random
  [2]: https://github.com/python/cpython/blob/main/Modules/_randommodule.c
  [3]: https://stats.stackexchange.com/questions/68218/sample-from-continuous-uniform-distribution-with-open-interval