### 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)

{
                "OutputName": "analytics_output_name",
                "S3Output": {
                    "LocalPath": "local_path",
                    "S3UploadMode": "EndOfJob",
                    "S3Uri": "s3_uri",
                },
            }
        ],
    },
    "ProcessingResources": {
        "ClusterConfig": {
            "InstanceCount": "2",
            "InstanceType": "ml.p2.xlarge",
            "VolumeSizeInGB": "30",
            "VolumeKmsKeyId": "kms_key",
        }
    },
    "RoleArn": "arn:aws:iam::0122345678910:role/SageMakerPowerUser",
    "Tags": [{"key": "value"}],
}
CREATE_PROCESSING_PARAMS_WITH_STOPPING_CONDITION: dict = CREATE_PROCESSING_PARAMS.copy()
CREATE_PROCESSING_PARAMS_WITH_STOPPING_CONDITION.update(StoppingCondition={"MaxRuntimeInSeconds": "3600"})
EXPECTED_INTEGER_FIELDS: list[list[str]] = [
    ["ProcessingResources", "ClusterConfig", "InstanceCount"],
    ["ProcessingResources", "ClusterConfig", "VolumeSizeInGB"],
]
EXPECTED_STOPPING_CONDITION_INTEGER_FIELDS: list[list[str]] = [["StoppingCondition", "MaxRuntimeInSeconds"]]
class TestSageMakerProcessingOperator(unittest.TestCase):
    def setUp(self):