Disallow zero dimensions in vectors and memrefs

Aggregate types where at least one dimension is zero do not fully make sense as
they cannot contain any values (their total size is zero).  However, TensorFlow
and XLA support tensors with zero sizes, so we must support those too.  This is
relatively safe since, unlike vectors and memrefs, we don't have first-class
element accessors for MLIR tensors.

To support sparse element attributes of vector types that have no non-zero
elements, make sure that index and value element attributes have tensor type so
that we never need to create a zero vector type internally.  Note that this is
already consistent with the inline documentation of the sparse elements
attribute.  Users of the sparse elements attribute should not rely on the
storage schema anyway.

PiperOrigin-RevId: 232896707