[mlir][linalg] Update vectorization of linalg.pack (#163539)

This patch changes `vectorizeAsTensorPackOp` to require users to specify
**all** write-side vector sizes for `linalg.pack` (not just the outer
dimensions). This makes `linalg.pack` vectorization consistent with
`linalg.unpack` (see https://github.com/llvm/llvm-project/pull/149293
for a similar change).

Conceptually, `linalg.pack` consists of these high-level steps:
  * **Read** from the source tensor using `vector.transfer_read`.
  * **Re-associate** dimensions of the read value, as specified by
    the op (via `vector.shape_cast`)
  * **Transpose** the re-associated value according to the permutation
    in the `linalg.pack` op (via `vector.transpose`).
  * **Write** the result into the destination tensor via
    `vector.transfer_write`.

Previously, the vector sizes provided by the user were interpreted as
write-vector-sizes for PackOp **_outer_** dims (i.e. the final step
above). These were used to:
  * Infer read-vector-sizes using the `inner_tiles` attribute of PackOp.
  * Deduce vector sizes for the transpose and shape cast operations.
  * Ultimately determine the vector shape for the read.

However, this logic breaks when one or more tile sizes are dynamic (*).
In such cases, `vectorizePackOpPrecondition` would currently fail (see
`@pack_with_dynamic_dims_and_dynamic_inner_tile` added in this PR -
without this change it will crash).

This patch updates the contract: users now directly specify _all_ the
"write-vector-sizes", which inherently encode all inner tile sizes -
including dynamic ones. It becomes the user's responsibility to provide
valid sizes.

In practice, since `linalg.pack` is typically constructed, tiled, and
vectorized by the same transformation pipeline, the necessary
"write-vector-sizes" should be recoverable.

Notes for reviewers:
  * See test updates for user-facing impact.
  * Review `vectorizeAsTensorPackOp` as a new implementation rather than
    a diff.
  * Comments and variable names were updated to align with
    `vectorizeAsTensorUnPackOp`.

(*) As a concrete example, "scalable" tile sizes are represent as
dynamic values. Note, support for "scalable" vectorisation will be added
in a separate PR.