**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
:param streams: A list of the log stream names. The position of the stream in this list is
            the stream number.
        :type streams: list
        :param positions: A list of pairs of (timestamp, skip) which represents the last record
            read from each stream.
        :type positions: list
        :return: A tuple of (stream number, cloudwatch log event).
        """
        positions = positions or {s: Position(timestamp=0, skip=0) for s in streams}
        event_iters = [
            self.logs_hook.get_log_events(log_group, s, positions[s].timestamp, positions[s].skip)
            for s in streams
        ]
        events: List[Optional[Any]] = []
        for event_stream in event_iters:
            if not event_stream:
                events.append(None)
                continue
            try:
                events.append(next(event_stream))
            except StopIteration:
                events.append(None)
        while any(events):
            i = argmin(events, lambda x: x['timestamp'] if x else 9999999999) or 0
            yield i, events[i]
            try:
                events[i] = next(event_iters[i])
            except StopIteration:
                events[i] = None
    def create_training_job(