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

Commencez dans sagemaker-core/src/sagemaker/core/spark/processing.py, autour de la construction de ProcessingOutput pour le journal d’événements de PySparkProcessor. Reproduisez l’échec avec une spark_event_logs_s3_uri valide et examinez l’interface actuelle de ProcessingOutput. La tâche est terminée lorsque le job PySparkProcessor démarre correctement avec les journaux d’événements Spark configurés.

Rédigé par le modèle d'indexation à partir du texte de l'issue.