### Description

Airflow provides the Amazon DynamoDB to Amazon S3 below.  
https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/transfer/dynamodb_to_s3.html

Most of Data Engineer build their "export DDB data to s3" pipeline using "within the point in time recovery window".   
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb.html#DynamoDB.Client.export_table_to_point_in_time

I appreciate if airflow has this function as a native function.  

### Use case/motivation

My daily batch job exports its data with pitr option. All of tasks is written by apache-airflow-providers-amazon except "export_table_to_point_in_time" task. 
"export_table_to_point_in_time" task only used the python operator. I expect I can unify the task as apache-airflow-providers-amazon library. 

### Related issues

_No response_

### Are you willing to submit a 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)

def test_init(self):
        waiter_name = "test_waiter"
        client_name = "test_client"
        waiter_model_config = {
            "version": 2,
            "waiters": {
                waiter_name: {
                    "operation": "ListNodegroups",
                    "delay": 30,
                    "maxAttempts": 60,
                    "acceptors": [
                        {
                            "matcher": "path",
                            "argument": "length(nodegroups[]) == `0`",
                            "expected": True,
                            "state": "success",
                        },
                        {
                            "matcher": "path",
                            "expected": True,
                            "argument": "length(nodegroups[]) > `0`",
                            "state": "retry",
                        },
                    ],
                }
            },
        }
        expected_model = WaiterModel(waiter_model_config)
        waiter = BaseBotoWaiter(client_name, waiter_model_config)