### Apache Airflow Provider(s)

amazon

### Versions of Apache Airflow Providers



apache-airflow-providers-amazon | 2.4.0

### Apache Airflow version

2.2.3 (latest released)

### Operating System

Amazon Linux 2

### Deployment

MWAA

### Deployment details

_No response_

### What happened

Sagemaker Processing Operator no longer honors the `action_if_job_exists` param and always fails creation of a new processing job is a job with the name already exists. 

This happens because in a recent change, the function responsible for executing the job no longer honors the increment setting:

Change that breaks the increment: https://github.com/apache/airflow/commit/96dd70348ad7e31cfeae6d21af70671b41551fe9

New code: https://github.com/apache/airflow/blob/6734eb1d09a99dc519e89a59e2086cef09a87098/airflow/providers/amazon/aws/operators/sagemaker.py#L167

  

### What you expected to happen

When Sagemaker Processing operator is called with a job-name that already exists, the job creation should succeed with a name that is incremented by 1.   

### How to reproduce

invoke SageMakerProcessingOperator twice with the same job name while keeping action_if_job_exists as 'increment'.   

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

config: dict,
        wait_for_completion: bool = True,
        check_interval: int = 30,
        max_ingestion_time: int | None = None,
    ):
        """
        Deploys the new EndpointConfig specified in the request, switches to using
        newly created endpoint, and then deletes resources provisioned for the
        endpoint using the previous EndpointConfig (there is no availability loss).
        :param config: the config for endpoint
        :param wait_for_completion: if the program should keep running until job finishes
        :param check_interval: the time interval in seconds which the operator
            will check the status of any SageMaker job
        :param max_ingestion_time: the maximum ingestion time in seconds. Any
            SageMaker jobs that run longer than this will fail. Setting this to
            None implies no timeout for any SageMaker job.
        :return: A response to endpoint update
        """
        response = self.get_conn().update_endpoint(**config)
        if wait_for_completion:
            self.check_status(
                config["EndpointName"],
                "EndpointStatus",
                self.describe_endpoint,
                check_interval,
                max_ingestion_time,
                non_terminal_states=self.endpoint_non_terminal_states,
            )
        return response
    def describe_training_job(self, name: str):