Cannot use spark_event_logs_s3_uri in PySparkProcessor job

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

**Describe the bug**

Similar to #6252 - PySparkProcessor `run` no longer handles `spark_event_logs_s3_uri` due to changes in the `ProcessingOutput` interface - e.g. regression introduced in 3203e49

**To reproduce**

Provide any (valid) S3 URI for the spark log location.

**Expected behavior**

Successfully starts the job.

**Screenshots or logs**

```
ValidationError: 4 validation errors for ProcessingOutput
output_name
Field required [type=missing, input_value={'source': '/opt/ml/proce...oad_mode': 'Continuous'}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.13/v/missing
source
Extra inputs are not permitted [type=extra_forbidden, input_value='/opt/ml/processing/spark-events/', input_type=str]
For further information visit https://errors.pydantic.dev/2.13/v/extra_forbidden
destination
Extra inputs are not permitted [type=extra_forbidden, input_value='s3://...', input_type=str]
For further information visit https://errors.pydantic.dev/2.13/v/extra_forbidden
s3_upload_mode
Extra inputs are not permitted [type=extra_forbidden, input_value='Continuous', input_type=str]
For further information visit https://errors.pydantic.dev/2.13/v/extra_forbidden
```

The cause of the error is [here](https://github.com/aws/sagemaker-python-sdk/blob/8e7485a1ed25eb17c70f2323ab6ed6c9695d7024/sagemaker-core/src/sagemaker/core/spark/processing.py#L293-L297):

```py
output = ProcessingOutput(
source=_SparkProcessorBase._spark_event_log_default_local_path,
destination=spark_event_logs_s3_uri,
s3_upload_mode="Continuous",
)
```

**System information**

- AWS SageMaker Studio 4.4.3
- sagemaker-core: 2.20
- Python 3.12.14
- uname: Linux default 6.12.103-127.188.amzn2023.x86_64 SMP PREEMPT_DYNAMIC Tue Aug 25 15:42:53 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux

Comienza en sagemaker-core/src/sagemaker/core/spark/processing.py, en torno a la construcción de ProcessingOutput para el registro de eventos de PySparkProcessor. Reproduce el fallo con una spark_event_logs_s3_uri válida e inspecciona la interfaz actual de ProcessingOutput. La tarea está terminada cuando el trabajo de PySparkProcessor se inicia correctamente con los registros de eventos de Spark configurados.

Escrito por el modelo de indexación a partir del texto del issue.