QualityCheckStep fails with ValidationError when baseline_dataset is a pipeline variable

**PySDK Version**
- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**

`QualityCheckStep._generate_baseline_job_inputs()` creates a `ProcessingInput` with an incomplete `s3_input` dict when `baseline_dataset` is a pipeline variable (e.g. `Join`, `ParameterString`). The dict is missing the required `s3_data_type` field, causing a Pydantic `ValidationError`.

The bug is in [`sagemaker-mlops/src/sagemaker/mlops/workflow/quality_check_step.py` line 354](https://github.com/aws/sagemaker-python-sdk/blob/a24812666d258d9d19c9a82d0c9385ca0bd3ba01/sagemaker-mlops/src/sagemaker/mlops/workflow/quality_check_step.py#L354):

```python
if is_pipeline_variable(baseline_dataset):
baseline_dataset_input = ProcessingInput(
input_name=_BASELINE_DATASET_INPUT_NAME,
s3_input={
"s3_uri": self.quality_check_config.baseline_dataset,
"local_path": baseline_dataset_des,
}
)
```

`ProcessingS3Input` (from `sagemaker.core.shapes`) requires `s3_data_type` as a mandatory field with no default. The `else` branch correctly provides it via `_upload_and_convert_to_processing_input()`, but the pipeline variable branch does not.

**To reproduce**

```python
import boto3
from sagemaker.core.helper.session_helper import Session
from sagemaker.core.workflow.functions import Join
from sagemaker.core.workflow.parameters import ParameterString
from sagemaker.core.workflow.pipeline_context import PipelineSession
from sagemaker.mlops.workflow.quality_check_step import DataQualityCheckConfig, QualityCheckStep
from sagemaker.mlops.workflow.check_job_config import CheckJobConfig

pipeline_session = PipelineSession(boto_session=boto3.Session())

param_endpoint_name = ParameterString(name="EndpointName")

# baseline_dataset is a pipeline variable — resolved at execution time
baseline_dataset_uri = Join(
on="/",
values=["s3:/", "my-bucket", param_endpoint_name, "baseline/dataset.parquet"],
)

quality_check_config = DataQualityCheckConfig(
baseline_dataset=baseline_dataset_uri,
dataset_format={"parquet": {}},
output_s3_uri="s3://my-bucket/output/",
)

check_job_config = CheckJobConfig(
role="arn:aws:iam::123456789012:role/SageMakerRole",
instance_count=1,
instance_type="ml.m5.xlarge",
sagemaker_session=pipeline_session,
)

# This raises ValidationError
step = QualityCheckStep(
name="compute-baseline",
quality_check_config=quality_check_config,
check_job_config=check_job_config,
skip_check=True,
register_new_baseline=True,
)
```

**Expected behavior**

`QualityCheckStep` should instantiate successfully when `baseline_dataset` is a pipeline variable. The fix is to include `s3_data_type` in the dict:

```python
if is_pipeline_variable(baseline_dataset):
baseline_dataset_input = ProcessingInput(
input_name=_BASELINE_DATASET_INPUT_NAME,
s3_input={
"s3_uri": self.quality_check_config.baseline_dataset,
"local_path": baseline_dataset_des,
"s3_data_type": "S3Prefix", # <-- missing
}
)
```

**Screenshots or logs**

```
ValidationError: 1 validation error for ProcessingInput
s3_input.s3_data_type
Field required [type=missing, input_value={'s3_uri': Join(on='/', v...baseline_dataset_input'}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.13/v/missing
```

**System information**
- **SageMaker Python SDK version**: sagemaker 3.20.0, sagemaker-mlops 1.20.0, sagemaker-core 2.3.0
- **Framework name**: N/A (SageMaker Model Monitor)
- **Framework version**: N/A
- **Python version**: 3.13.5
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N

**Additional context**

The bug is present on the latest `main` branch as well as all released versions of `sagemaker-mlops` (1.0–1.20.0). It only manifests when `baseline_dataset` is a pipeline variable (`Join`, `JsonGet`, `ParameterString`, etc.) — static string paths work fine because they take the `else` branch which uses `_upload_and_convert_to_processing_input()`.

Workaround: patch `ProcessingS3Input` to make `s3_data_type` optional before constructing the step:

```python
from sagemaker.core.shapes import ProcessingInput, ProcessingS3Input

ProcessingS3Input.model_fields["s3_data_type"].default = "S3Prefix"
ProcessingS3Input.model_rebuild(force=True)
ProcessingInput.model_rebuild(force=True)
```

Bắt đầu tại sagemaker-mlops/src/sagemaker/mlops/workflow/quality_check_step.py, khoảng dòng 354, và tái hiện việc khởi tạo QualityCheckStep với một Join hoặc ParameterString cho baseline_dataset. So sánh nhánh biến pipeline với _upload_and_convert_to_processing_input(); hoàn tất nghĩa là step được khởi tạo thành công và một kiểm tra hồi quy bao quát các trường ProcessingInput bắt buộc.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.