In VAE's, we want to model the distribution of images $x$ with some latent variable $z$. Because $x$ is a random variable, You can think of $P(x|z)$ as the distribution of images $x$ conditioned on the random variable $z$. So given a particular value of $z$, we can generate a distribution over images $x$.
VAE's try to model images, which are themselves high dimensional 2D data. Given a 28x28 image, we already have 784 latent variables to model. We cannot visualise the distribution over all images $x$. Your notation $P(x < X|z)$ makes sense in a 1D case with a scalar value. However when considering 2D and higher, we have a problem with how we consider what is less then. if $x = (y_1,y_2)$ and $X = (y_3,y_4)$, then is $x < X$ if both $y_1 < y_3$ and $y_2 < y_4$? (I.e all dimensions have to be less than or if only one dimension needs to be less than). When talking about high dimensional space therefore, it is not very useful to denote $P(x < X|z)$ because of difficulty in interpreting the results.