### 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)

"""
        Check if an S3 URL exists
        :param s3url: S3 url
        """
        bucket, key = S3Hook.parse_s3_url(s3url)
        if not self.s3_hook.check_for_bucket(bucket_name=bucket):
            raise AirflowException(f"The input S3 Bucket {bucket} does not exist ")
        if (
            key
            and not self.s3_hook.check_for_key(key=key, bucket_name=bucket)
            and not self.s3_hook.check_for_prefix(prefix=key, bucket_name=bucket, delimiter="/")
        ):
            
            
            
            raise AirflowException(
                f"The input S3 Key or Prefix {s3url} does not exist in the Bucket {bucket}"
            )
        return True
    def check_training_config(self, training_config: dict) -> None:
        """
        Check if a training configuration is valid
        :param training_config: training_config
        :return: None
        """
        if "InputDataConfig" in training_config:
            for channel in training_config["InputDataConfig"]:
                if "S3DataSource" in channel["DataSource"]:
                    self.check_s3_url(channel["DataSource"]["S3DataSource"]["S3Uri"])
    def check_tuning_config(self, tuning_config: dict) -> None: