[BUG] Sparse initializers causes Type Error for SparseToDenseMatMul

### Describe the issue

I've created an ONNX model consisting solely of a SparseToDenseMatMul operator. A (data1) is a sparse initializer, and B (input1) is an input. Running the model results in the error:

> This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (data1) of operator (SparseToDenseMatMul) in node (SparseToDenseMatMul1) is invalid.

It says data1 is a tensor and not a sparse_tensor. However, when I inspect the model using Netron, it shows "category: Initializer" and "layout: sparse".

### To reproduce

Attached are two models: sparse_matmul_julia.onnx created using my Julia code, and sparse_matmul.onnx created using some LLM-generated Python code below. The models are not identical but give the same error.
[models.zip](https://github.com/user-attachments/files/30855759/models.zip)

```python
import numpy as np
import onnx
from onnx import helper, TensorProto
import onnxruntime as ort

A_values = helper.make_tensor(
name="data1",
data_type=TensorProto.FLOAT,
dims=[4],
vals=[1.0, 2.0, 3.0, 4.0],
)

A_indices = helper.make_tensor(
name="data1_indices",
data_type=TensorProto.INT64,
dims=[4, 2],
vals=[
0, 0,
0, 2,
1, 1,
1, 3,
],
)

A = onnx.SparseTensorProto()
A.values.CopyFrom(A_values)
A.indices.CopyFrom(A_indices)
A.dims.extend([2, 4])

B = helper.make_tensor(
name="input1",
data_type=TensorProto.FLOAT,
dims=[4, 3],
vals=[
1.0, 2.0, 3.0,
4.0, 5.0, 6.0,
7.0, 8.0, 9.0,
10.0, 11.0, 12.0,
],
)

Y = helper.make_tensor_value_info(
"output",
TensorProto.FLOAT,
[2, 3],
)

node = helper.make_node(
"SparseToDenseMatMul",
inputs=["data1", "input1"],
outputs=["output"],
name="SparseToDenseMatMul1",
domain="com.microsoft",
)

graph = helper.make_graph(
nodes=[node],
name="SparseMatMulExample",
inputs=[],
outputs=[Y],
initializer=[B],
sparse_initializer=[A],
)

model = helper.make_model(
graph,
producer_name="python-test",
opset_imports=[
helper.make_operatorsetid("", 21),
helper.make_operatorsetid("com.microsoft", 1),
],
)

# Check the protobuf structure.
onnx.checker.check_model(model)

onnx.save(model, "sparse_matmul.onnx")

# Load and run the model with onnxruntime
sess = ort.InferenceSession("sparse_matmul.onnx", providers=["CPUExecutionProvider"])
outputs = sess.run(None, {})
print(outputs[0])
```

### Urgency

No urgency.

### Platform

Windows

### OS Version

Microsoft Windows 11 Home

### ONNX Runtime Installation

Released Package

### ONNX Runtime Version or Commit ID

1.24.4

### ONNX Runtime API

Python

### Architecture

X64

### Execution Provider

Default CPU

### Execution Provider Library Version

_No response_

Start with the attached models and the Python reproduction using ONNX Runtime 1.24.4, then trace validation and execution for the com.microsoft SparseToDenseMatMul operator with data1 supplied as a sparse_initializer. Confirm the cause of the tensor-versus-sparse_tensor type error and verify that the reproduced model loads and runs successfully afterward.

Written by the indexing model from the issue text.