[QNN QDQ Quant] Utils to generate mixed-precision quant overrides (#2…

…0028)

### Description
- Adds a utility to the QNN quantization scripts that "fixes" an initial
set of tensor quantization overrides for mixed-precision QDQ models.
Follow-up to #19925
- Moves existing overrides for QNN compatibility (matmul, layernorm,
sigmoid, tanh) to separate functions. PR adds missing unit tests for
these.
- Adds `weight_symmetric=None` parameter to the `get_qnn_qdq_config()`
function to enable user specification (instead of always using default
behavior).
- If weight_symmetric is set to `None`, it will be set to
`weight_symmetric = weight_type in (QUInt8, QUInt16)`.
  - Otherwise, the user's value is used.

#### Example
Float model:

```
    input_0 --> Op1 --> Op3 --> Op5 --> Op6 --> output_0
                                 ^
                                 |
    input_1 --> Op2 -+-> Op4 ----+
                     |
                     +-> Op7 --> output_1
                     |
                     +-> Op8 --> output_2
```

If we'd like to quantize this model to uint8 precision, but would like
to make sure tensor "Op4_out" is quantized to 16-bit, then we would
specify the following initial tensor quantization overrides:
```python
# Op4_out could be an inaccurate tensor that should be upgraded to 16bit
initial_overrides = {"Op4_out": [{"quant_type": QuantType.QUInt16}]}
```

These initial overrides may not create a valid model because Op4 and Op5
may require both the input and output to be the same type (e.g.,
uint16). This helper fixes the overrides so that input/output data types
are valid:

```python
qnn_config = get_qnn_qdq_config(
    float_model_path,
    data_reader,
    activation_type=QuantType.QUInt8,
    weight_type=QuantType.QUInt8,
    init_overrides=initial_overrides,  # These initial overrides will be "fixed"
)
```

The above snippet generates the following "fixed" overrides (get via
`qnn_config.extra_options["TensorQuantOverrides"]`):
```python
    {
      "Op2_out": [{"quant_type": QUInt8, "convert": {"quant_type": QUInt16, "recv_nodes": {"Op4"}}}],
      "Op3_out": [{"quant_type": QUInt8, "convert": {"quant_type": QUInt16, "recv_nodes": {"Op5"}}}],
      "Op4_out": [{"quant_type": QUInt16}],
      "Op5_out": [{"quant_type": QUInt16, "convert": {"quant_type": QUInt8, "recv_nodes": {"Op6"}}}]
    }
```

How to interpret the fixed overrides:
- Op2's output is consumed by Op4, Op7, and Op8. Op4 consumes the
converted u16 type, but Op7 and Op8 consume the original u8 type.
- Op3's output is converted from u8 to u16. Op5 consumes the converted
u16 type.
- Op4's output is just u16 (not converted). All consumers of Op4_out get
the u16 type.
- Op5's output is converted from u16 to u8. Op6 consumes the u8 type.

### Motivation and Context
Generating mixed-precision quantization overrides is currently a manual
process. This PR adds an utility that helps generate valid overrides.