**Apache Airflow version**: 2.0.2


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): NA

**Environment**: MWAA and Locally

- **Cloud provider or hardware configuration**: AWS
- **OS** (e.g. from /etc/os-release): NA
- **Kernel** (e.g. `uname -a`): NA
- **Install tools**: NA
- **Others**: NA

**What happened**:

When calling the SagemakerProcessingOperator sometimes get: "botocore.exceptions.ClientError: An error occurred (ThrottlingException)" due to excessive ListProcessingJobs operations.

**What you expected to happen**:

The job should have started without timing out. I believe one fix would be to use the `NameContains` functionality of boto3 [list_processing_jobs](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.list_processing_jobs) so you don't have to paginate as is occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/sagemaker.py#L916). 

**How to reproduce it**:

If you incrementally create Sagemaker Processing jobs you will eventually see the Throttling as the pagination increases.

**Anything else we need to know**:

This looks like it is happening when the account already has a lot of former Sagemaker Processing jobs.
airflow/providers/amazon/aws/hooks/sagemaker.py
super().__init__(client_type='sagemaker', *args, **kwargs)
        self.s3_hook = S3Hook(aws_conn_id=self.aws_conn_id)
        self.logs_hook = AwsLogsHook(aws_conn_id=self.aws_conn_id)
    def tar_and_s3_upload(self, path: str, key: str, bucket: str) -> None:
        """
        Tar the local file or directory and upload to s3
        :param path: local file or directory
        :type path: str
        :param key: s3 key
        :type key: str
        :param bucket: s3 bucket
        :type bucket: str
        :return: None
        """
        with tempfile.TemporaryFile() as temp_file:
            if os.path.isdir(path):
                files = [os.path.join(path, name) for name in os.listdir(path)]
            else:
                files = [path]
            with tarfile.open(mode='w:gz', fileobj=temp_file) as tar_file:
                for f in files:
                    tar_file.add(f, arcname=os.path.basename(f))
            temp_file.seek(0)
            self.s3_hook.load_file_obj(temp_file, key, bucket, replace=True)
    def configure_s3_resources(self, config: dict) -> None: