Fix sparse initializer type for SparseToDenseMatMul

[NIKHIL (Nikhi00718)]

Description

Sparse initializers are currently converted to dense TensorProto values during graph construction, but their NodeArg type is inferred as tensor(T) instead of sparse_tensor(T). As a result, a valid SparseToDenseMatMul model that references a sparse initializer is rejected during graph validation with a type error before the kernel is created.

This change preserves the sparse type and shape metadata for sparse initializers both when NodeArgs are created and when initializer types are checked. Runtime materialization already uses the graph's sparse-initializer marker to create a SparseTensor OrtValue, so the existing execution path can handle the model once validation succeeds.

Fixes #31730.

Tests

• Added a regression test that builds the issue reporter's sparse-initializer model in memory, saves it to a temporary ONNX file, runs it with the CPU execution provider, and checks the matrix result.
• python -m py_compile onnxruntime/test/python/onnxruntime_test_python_sparse_matmul.py
• git diff --check

A native ONNX Runtime build was not available in the Windows environment used to prepare this pull request; the new test is intended for the repository's native CI matrix.

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