### 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)

"""
    Starts a transform job. A transform job uses a trained model to get inferences
    on a dataset and saves these results to an Amazon S3 location that you specify.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerTransformOperator`
    :param config: The configuration necessary to start a transform job (templated).
        If you need to create a SageMaker transform job based on an existed SageMaker model::
            config = transform_config
        If you need to create both SageMaker model and SageMaker Transform job::
            config = {
                'Model': model_config,
                'Transform': transform_config
            }
        For details of the configuration parameter of transform_config see
        :py:meth:`SageMaker.Client.create_transform_job`
        For details of the configuration parameter of model_config, See:
        :py:meth:`SageMaker.Client.create_model`
    :param aws_conn_id: The AWS connection ID to use.
    :param wait_for_completion: Set to True to wait until the transform job finishes.
    :param check_interval: If wait is set to True, the time interval, in seconds,
        that this operation waits to check the status of the transform job.
    :param max_ingestion_time: If wait is set to True, the operation fails
        if the transform job doesn't finish within max_ingestion_time seconds. If you
        set this parameter to None, the operation does not timeout.
    :param check_if_job_exists: If set to true, then the operator will check whether a transform job
        already exists for the name in the config.
    :param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"