### 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)

: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.
        :param non_terminal_states: the set of nonterminal states
        :return: response of describe call after job is done
        """
        if not non_terminal_states:
            non_terminal_states = self.non_terminal_states
        sec = 0
        while True:
            time.sleep(check_interval)
            sec += check_interval
            try:
                response = describe_function(job_name)
                status = response[key]
                self.log.info("Job still running for %s seconds... current status is %s", sec, status)
            except KeyError:
                raise AirflowException("Could not get status of the SageMaker job")
            except ClientError:
                raise AirflowException("AWS request failed, check logs for more info")
            if status in self.failed_states:
                raise AirflowException(f"SageMaker job failed because {response['FailureReason']}")
            elif status not in non_terminal_states:
                break
            if max_ingestion_time and sec > max_ingestion_time:
                
                raise AirflowException(f"SageMaker job took more than {max_ingestion_time} seconds")
        self.log.info("SageMaker Job completed")
        return response
    def check_training_status_with_log(