### Description

Add a process_line callback to KubernetesPodOperator 
Like https://github.com/apache/airflow/blob/main/airflow/providers/apache/beam/operators/beam.py#LL304C36-L304C57 the `BeamRunPythonPipelineOperator`, which allows the user to add stateful plugins based on the logging from docker job 

### Use case/motivation

I can add a plugin based on the logging and also allow cleanup on_kill based on the job creation log. 

### 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)

expected_dict = {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {
                "annotations": {},
                "labels": {
                    "test_label": test_label,
                    "airflow_kpo_in_cluster": "False",
                    "dag_id": "dag",
                    "run_id": "manual__2016-01-01T0100000100-da4d1ce7b",
                    "kubernetes_pod_operator": "True",
                    "task_id": mock.ANY,
                    "try_number": "1",
                },
                "name": "memory-demo",
                "namespace": "mem-example",
            },
            "spec": {
                "affinity": {},
                "containers": [
                    {
                        "args": ["--vm", "1", "--vm-bytes", "150M", "--vm-hang", "1"],
                        "command": ["stress"],
                        "env": [],
                        "envFrom": [],
                        "image": "ghcr.io/apache/airflow-stress:1.0.4-2021.07.04",
                        "name": "base",
                        "ports": [],
                        "resources": {"limits": {"memory": "200Mi"}, "requests": {"memory": "100Mi"}},
                        "terminationMessagePolicy": "File",