A better distinction would be that deep learning excels in application that require representation learning, i.e. transformation from domains that do not lie in Euclidean metric space (e.g. graphs) or that are too problematic in the raw form and require processing in another domain (e.g. images, audio). This is very similar to feature extraction, but representation learning is a bit more general term.

Tabular ML does not need this in general, since after obtaining feature vectors we already have a representation and deep learning like MLP can only apply (exponentially) nonlinear transformation of that space, instead of really learning fundamentally new representations of that data, which is the case e.g. for images, going from raw pixel values space into vector space that captures semantic features in the image.