### Description

Currently, KubernetesPodOperator fetches logs using
```
             self.pod_manager.fetch_container_logs(
                    pod=self.pod,
                    container_name=self.BASE_CONTAINER_NAME,
                    follow=True,
                )
```
and so only shows log from the main container in a pod. It would be very useful/helpful to have the possibility to fetch logs for all the containers in a pod.


### Use case/motivation

Making the cause of failed KubernetesPodOperator tasks a lot more visible.

### 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)

actual_pod = self.api_client.sanitize_for_serialization(k.pod)
        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"}},