[mlir][linalg] Split GenericPadOpVectorizationPattern into two patterns (#111349)

At the moment, `GenericPadOpVectorizationPattern` implements two
orthogonal transformations:
  1. Rewrites `tensor::PadOp` into a sequence of `tensor::EmptyOp`,
    `linalg::FillOp` and `tensor::InsertSliceOp`.
  2. Vectorizes (where possible) `tensor::InsertSliceOp` (see
    `tryVectorizeCopy`).

This patch splits `GenericPadOpVectorizationPattern` into two separate
patterns:
  1. `GeneralizePadOpPattern` for the first transformation (note that
    currently `GenericPadOpVectorizationPattern` inherits from
    `GeneralizePadOpPattern`).
  2. `InsertSliceVectorizePattern` to vectorize `tensor::InsertSliceOp`.

With this change, we gain the following:
  * a clear separation between pre-processing and vectorization
    transformations/stages,
  * a path to support masked vectorisation for `tensor.insert_slice`
    (with a dedicated pattern for vectorization, it is much easier to
    specify the input vector sizes used in masking),
  * more opportunities to vectorize `tensor.insert_slice`.

Note for downstream users:
--------------------------

If you were using `populatePadOpVectorizationPatterns`, following this
change you will also have to add
`populateInsertSliceVectorizationPatterns`.

Finer implementation details:
-----------------------------

1.  The majority of changes in this patch are copy & paste + some edits.
  1.1. The only functional change is that the vectorization of
    `tensor.insert_slice` is now broadly available (as opposed to being
    constrained to the pad vectorization pattern:
    `GenericPadOpVectorizationPattern`).
  1.2. Following-on from the above, `@pad_and_insert_slice_dest` is
    updated. As expected, the input `tensor.insert_slice` Op is no
    longer "preserved" and instead gets vectorized successfully.

2. The `linalg.fill` case in `getConstantPadVal` works under the
   assumption that only _scalar_ source values can be used. That's
   consistent with the definition of the Op, but it's not tested at the
   moment. Hence a test case in Linalg/invalid.mlir is added.

3. The behaviour of the two TD vectorization Ops,
   `transform.structured.vectorize_children_and_apply_patterns` and
   `transform.structured.vectorize` is preserved.

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