As far as I can tell, Bayesian Networks do not claim to be able to estimate causal effects in non-directed acyclic graphs, whereas SEM does. That's a generalization in favor of SEM... if you believe it.
An example of this might be measuring cognitive decline among people where cognition is a latent effect estimated using a survey instrument like 3MSE, but some people may display decreased cognition as a function of pain meds usage. Their pain meds may have been a consequence of injuring themselves due to cognitive decline (falling for example). And so, in a cross sectional analysis, you would see a graph that has a circular shape. SEM analysts like to tackle problems like that. I steer clear.
In the Bayes network world, you have very general methods of assessing conditional independence/dependence of nodes. One can use a fully parametric approach with any number of distributions, or go about the Bayesian nonparametric approaches I've heard about. SEM estimated using ML are (usually) assumed to be normal, which means that conditional independence is equivalent to having zero covariance for 2 nodes in the graph. I personally believe that's a rather strong assumption and would have very little robustness to model misspecification.