### 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)

"""
    Creates a model in Amazon SageMaker. In the request, you name the model and
    describe a primary container. For the primary container, you specify the Docker
    image that contains inference code, artifacts (from prior training), and a custom
    environment map that the inference code uses when you deploy the model for predictions.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerModelOperator`
    :param config: The configuration necessary to create a model.
        For details of the configuration parameter see :py:meth:`SageMaker.Client.create_model`
    :param aws_conn_id: The AWS connection ID to use.
    :return Dict: Returns The ARN of the model created in Amazon SageMaker.
    """
    def __init__(self, *, config: dict, aws_conn_id: str = DEFAULT_CONN_ID, **kwargs):
        super().__init__(config=config, **kwargs)
        self.aws_conn_id = aws_conn_id
    def expand_role(self) -> None:
        """Expands an IAM role name into an ARN."""
        if "ExecutionRoleArn" in self.config:
            hook = AwsBaseHook(self.aws_conn_id, client_type="iam")
            self.config["ExecutionRoleArn"] = hook.expand_role(self.config["ExecutionRoleArn"])
    def execute(self, context: Context) -> dict:
        self.preprocess_config()
        self.log.info("Creating SageMaker Model %s.", self.config["ModelName"])
        response = self.hook.create_model(self.config)
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Sagemaker model creation failed: {response}")
        else:
            return {"Model": serialize(self.hook.describe_model(self.config["ModelName"]))}
class SageMakerTrainingOperator(SageMakerBaseOperator):