### 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)

def create_model(self, config: dict):
        """
        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.
        :param config: the config for model
        :return: A response to model creation
        """
        return self.get_conn().create_model(**config)
    def create_endpoint_config(self, config: dict):
        """
        Creates an endpoint configuration that Amazon SageMaker hosting
        services uses to deploy models. In the configuration, you identify
        one or more models, created using the CreateModel API, to deploy and
        the resources that you want Amazon SageMaker to provision.
        .. seealso::
             :class:`~airflow.providers.amazon.aws.hooks.sagemaker.SageMakerHook.create_model`
             :class:`~airflow.providers.amazon.aws.hooks.sagemaker.SageMakerHook.create_endpoint`
        :param config: the config for endpoint-config
        :return: A response to endpoint config creation
        """
        return self.get_conn().create_endpoint_config(**config)
    def create_endpoint(
        self,
        config: dict,
        wait_for_completion: bool = True,
        check_interval: int = 30,
        max_ingestion_time: int | None = None,
    ):