[tf.data] Allow the DeserializeSparse op to accept inconsistent dense shapes.

This changes DeserializeSparse to match the behavior of DeserializeSparseMany
and TakeManySparseFromTensorsMap, and thus makes `Dataset.batch()` on sparse
tensors match the existing behavior of `tf.train.batch()` and family.

The rationale for this change is that the source of many `tf.SparseTensor`
objects is `tf.parse[_single]_example()`, and that operation does not try
to ensure that consecutive `SparseTensor` objects parsed from the same
feature specification have the same `dense_shape`. As a result, the behavior
of existing ops that batch `SparseTensor` objects has been to silently pad
those objects to the bounding dense_shape, by taking the maximum over each
dimension size. While this does reduce our ability to make consistency checks
in the `SparseTensor`-handling code, pragmatically we never get consistently
shaped `SparseTensor`s in real programs, so this seems like a reasonable path
for usability.

PiperOrigin-RevId: 176697720