[mlir][linalg] Enable Vectorization of 0-D tensor.extract (#119079)

This patch removes an assert in `vectorizeTensorExtract` that was
blocking
the vectorization of 0-D tensor.extract operations, e.g.:

```mlir
  %1 = tensor.extract %src[] : tensor<f32>
```

As demonstrated by the included tests, this case is already effectively
supported.

**Context**
The removed assert was introduced in #109580 as a guard, pending proper
support
and testing for 0-D tensors. This PR addresses that previously
undocumented
TODO. Apologies for the oversight!

**Updates and Tests**
* Revised the existing test `@negative_no_loop` to ensure the
  `vectorize_nd_extract` attribute is included, allowing the vectorizer
  to process it. The test was renamed and variables updated for clarity.
* Added a new test `@extract_scalar_from_0d_into_1d` to cover "mixed"
  0-D/1-D tensor extraction, e.g.:
```mlir
  %res = linalg.generic {
    indexing_maps = [#map],
    iterator_types = ["parallel"]
  } outs(%init : tensor<1xf32>) {
  ^bb0(%in: f32):
    %1 = tensor.extract %src[] : tensor<f32>
    linalg.yield %1 : f32
  } -> tensor<1xf32>

  return %res : tensor<1xf32>
```

**Additional updates**
I also took the liberty and improved test coverage for 0-D tensor in the
vectorizer tests:
* Added a specific test for "0D linalg.generic" in
  "vectorization-with-patterns.mlir".
* Renamed several tests in "vectorization-with-patterns.mlir" to clarify
  that the 0-D case is now covered.