### Description

Today, Amazon provider package supports SageMaker operators for a few operations, like training, tuning, pipelines, but it lacks the support for SageMaker Notebook instances. Boto3 provides necessary APIs to [create notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_notebook_instance.html), [start notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/start_notebook_instance.html), [stop network instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/stop_notebook_instance.html) and [delete notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/delete_notebook_instance.html). Leveraging these APIs, we should add new operators to SageMaker set under Amazon provider. At the same time, having a sensor (synchronous as well as deferrable) for notebook instance execution that utilizes [describe notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_notebook_instance.html) and waits for Stopped/Failed status would help with observability of the execution.

### Use case/motivation

Data Scientists are orchestrating ML use cases via Apache Airflow. A key component of ML use cases is running Jupyter Notebook on SageMaker. Having built-in operators and sensors would make it easy for Airflow users to run Notebook instances on SageMaker.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] 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)

raise AirflowException(f"Sagemaker transform Job creation failed: {response}")
        if self.deferrable and self.wait_for_completion:
            self.defer(
                timeout=self.execution_timeout,
                trigger=SageMakerTrigger(
                    job_name=transform_config["TransformJobName"],
                    job_type="Transform",
                    poke_interval=self.check_interval,
                    max_attempts=self.max_attempts,
                    aws_conn_id=self.aws_conn_id,
                ),
                method_name="execute_complete",
            )
        self.serialized_model = serialize(self.hook.describe_model(transform_config["ModelName"]))
        self.serialized_tranform = serialize(
            self.hook.describe_transform_job(transform_config["TransformJobName"])
        )
        return {"Model": self.serialized_model, "Transform": self.serialized_tranform}
    def execute_complete(self, context, event=None):
        if event["status"] != "success":
            raise AirflowException(f"Error while running job: {event}")
        else:
            self.log.info(event["message"])
        transform_config = self.config.get("Transform", self.config)
        self.serialized_model = serialize(self.hook.describe_model(transform_config["ModelName"]))
        self.serialized_tranform = serialize(
            self.hook.describe_transform_job(transform_config["TransformJobName"])
        )
        return {"Model": self.serialized_model, "Transform": self.serialized_tranform}
    def get_openlineage_facets_on_complete(self, task_instance) -> OperatorLineage: