It doesn't work that way, and it is common to test for known imbalances, or to implement stratified sampling. As another poster said, confounders do not have to be conveniently distributed, even though that is typically assumed. It could be that a big effect is present in a confounder but collected only every N samples because it is sparse. In which case you could have large, randomly allocated but confounded samples.
All these things can and do happen in randomised experiments, but it is still orders of magnitude more interpretable than what can happen in observational studies.
Sure, if you correctly implement stratified sampling that’s fine. But then you’ve replaced the statistical machinery with one that takes this balancing into account.
If on the other hand you just redo the sampling until it seems balanced then you’ve violated the assumptions behind the standard statistical tooling.
All these things can and do happen in randomised experiments, but it is still orders of magnitude more interpretable than what can happen in observational studies.