### Description

Make resources in KubernetesPodOperator as templated. We need to modify this during several runs and it needs code change for each run.

### Use case/motivation

For running CPU and memory intensive workloads, we want to continuously optimise the "limt_cpu" and "limit_memory" parameters. Hence, we want to provide these parameters as a part of the pipeline definition.

### 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)

pod_phase = remote_pod.status.phase if hasattr(remote_pod, "status") else None
        if not self.is_delete_operator_pod:
            with _suppress(Exception):
                self.patch_already_checked(remote_pod)
        if pod_phase != PodPhase.SUCCEEDED:
            if self.log_events_on_failure:
                with _suppress(Exception):
                    for event in self.pod_manager.read_pod_events(pod).items:
                        self.log.error("Pod Event: %s - %s", event.reason, event.message)
            with _suppress(Exception):
                self.process_pod_deletion(remote_pod)
            error_message = get_container_termination_message(remote_pod, self.BASE_CONTAINER_NAME)
            error_message = "\n" + error_message if error_message else ""
            raise AirflowException(
                f"Pod {pod and pod.metadata.name} returned a failure:{error_message}\n{remote_pod}"
            )
        else:
            with _suppress(Exception):
                self.process_pod_deletion(remote_pod)
    def process_pod_deletion(self, pod: k8s.V1Pod):
        if pod is not None:
            if self.is_delete_operator_pod:
                self.log.info("Deleting pod: %s", pod.metadata.name)
                self.pod_manager.delete_pod(pod)
            else:
                self.log.info("skipping deleting pod: %s", pod.metadata.name)
    def _build_find_pod_label_selector(self, context: Context | None = None, *, exclude_checked=True) -> str: