[mlir][linalg] Fix padding shape computation in PadTilingInterface for convs (#149576)

This PR fixes the computation of padded shapes for convolution-style
affine maps (e.g., d0 + d1) in `PadTilingInterface`. Previously, the
codes used the direct sum of loop upper bounds, leading to over-padding.
For example, the following `conv_2d_nhwc_fhwc` op, if only padding the c
dimensions to multiples of 16, it also incorrectly pads the convolved
dimensions and generates the wrong input shape as:

```
%padded = tensor.pad %arg0 low[0, 0, 0, 0] high[0, 1, 1, 12] {
^bb0(%arg3: index, %arg4: index, %arg5: index, %arg6: index):
  tensor.yield %cst : f32
} : tensor<1x16x16x4xf32> to tensor<1x17x17x16xf32>
%padded_0 = tensor.pad %arg1 low[0, 0, 0, 0] high[0, 0, 0, 12] {
^bb0(%arg3: index, %arg4: index, %arg5: index, %arg6: index):
  tensor.yield %cst : f32
} : tensor<16x3x3x4xf32> to tensor<16x3x3x16xf32>
%0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%padded, %padded_0 : tensor<1x17x17x16xf32>, tensor<16x3x3x16xf32>) outs(%arg2 : tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32>
return %0 : tensor<1x14x14x16xf32>
```

The new implementation uses the maximum accessed index as the input for
affine map and then adds 1 after aggregating all the terms to get the
final padded size. This fixed
https://github.com/llvm/llvm-project/issues/148679.

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