### 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)

"""
    When you create a serverless endpoint, SageMaker provisions and manages
    the compute resources for you. Then, you can make inference requests to
    the endpoint and receive model predictions in response. SageMaker scales
    the compute resources up and down as needed to handle your request traffic.
    Requires an Endpoint Config.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerEndpointOperator`
    :param config:
        The configuration necessary to create an endpoint.
        If you need to create a SageMaker endpoint based on an existed
        SageMaker model and an existed SageMaker endpoint config::
            config = endpoint_configuration;
        If you need to create all of SageMaker model, SageMaker endpoint-config and SageMaker endpoint::
            config = {
                'Model': model_configuration,
                'EndpointConfig': endpoint_config_configuration,
                'Endpoint': endpoint_configuration
            }
        For details of the configuration parameter of model_configuration see
        :py:meth:`SageMaker.Client.create_model`
        For details of the configuration parameter of endpoint_config_configuration see
        :py:meth:`SageMaker.Client.create_endpoint_config`
        For details of the configuration parameter of endpoint_configuration see