**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
self,
        job_name: str,
        positions,
        stream_names: list,
        instance_count: int,
        state: int,
        last_description: dict,
        last_describe_job_call: float,
    ):
        """Return the training job info associated with job_name and print CloudWatch logs"""
        log_group = '/aws/sagemaker/TrainingJobs'
        if len(stream_names) < instance_count:
            
            
            logs_conn = self.logs_hook.get_conn()
            try:
                streams = logs_conn.describe_log_streams(
                    logGroupName=log_group,
                    logStreamNamePrefix=job_name + '/',
                    orderBy='LogStreamName',
                    limit=instance_count,
                )
                stream_names = [s['logStreamName'] for s in streams['logStreams']]