How do I right feature selection for DBSCAN?
Automatic weighting will likely not be enough.

For examples standard scaler will assign twice as much weight to the one-hot encoded parts than to the other attributes. Plus, it is based on the assumption that all variables should have the same (assuming they are all normal distributed) weight. But should they all have the same weight? Does it even make sense to use distances of one-hot encoded data?

You are not ready to use DBSCAN (or any other clustering) if you don't have a reliable distance yet that discriminates similar from dissimilar instances.