**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
max_results = kwargs["MaxResults"]
            del kwargs["MaxResults"]
        config.update(kwargs)
        list_training_jobs_request = partial(self.get_conn().list_training_jobs, **config)
        results = self._list_request(
            list_training_jobs_request, "TrainingJobSummaries", max_results=max_results
        )
        return results
    def list_processing_jobs(self, **kwargs) -> List[Dict]:
        """
        This method wraps boto3's `list_processing_jobs`. All arguments should be provided via kwargs.
        Note boto3 expects these in CamelCase format, for example:
        .. code-block:: python
            list_processing_jobs(NameContains="myjob", StatusEquals="Failed")
        .. seealso::
            https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.list_processing_jobs
        :param kwargs: (optional) kwargs to boto3's list_training_jobs method
        :return: results of the list_processing_jobs request
        """
        list_processing_jobs_request = partial(self.get_conn().list_processing_jobs, **kwargs)
        results = self._list_request(
            list_processing_jobs_request, "ProcessingJobSummaries", max_results=kwargs.get("MaxResults")
        )
        return results
    def _list_request(
        self, partial_func: Callable, result_key: str, max_results: Optional[int] = None
    ) -> List[Dict]:
        """
        All AWS boto3 list_* requests return results in batches (if the key "NextToken" is contained in the