### 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)

mock_session = mock.Mock()
        mock_log_session = mock.Mock()
        attrs = {"describe_training_job.return_value": DESCRIBE_TRAINING_COMPLETED_RETURN}
        log_attrs = {
            "describe_log_streams.side_effect": LIFECYCLE_LOG_STREAMS,
            "get_log_events.side_effect": STREAM_LOG_EVENTS,
        }
        mock_session.configure_mock(**attrs)
        mock_client.return_value = mock_session
        mock_log_session.configure_mock(**log_attrs)
        mock_log_client.return_value = mock_log_session
        hook = SageMakerHook(aws_conn_id="sagemaker_test_conn_id")
        response = hook.describe_training_job_with_log(
            job_name=job_name,
            positions={},
            stream_names=[],
            instance_count=1,
            state=log_state,
            last_description={},
            last_describe_job_call=0,
        )
        assert response == (LogState.COMPLETE, {}, 0)
    @mock.patch.object(SageMakerHook, "check_training_config")
    @mock.patch.object(AwsLogsHook, "get_conn")
    @mock.patch.object(SageMakerHook, "get_conn")
    @mock.patch.object(SageMakerHook, "describe_training_job_with_log")
    def test_training_with_logs(self, mock_describe, mock_client, mock_log_client, mock_check_training):