### 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)

"""
        Returns V1Pod object based on pod template file, full pod spec, and other operator parameters.
        The V1Pod attributes are derived (in order of precedence) from operator params, full pod spec, pod
        template file.
        """
        self.log.debug("Creating pod for KubernetesPodOperator task %s", self.task_id)
        if self.pod_template_file:
            self.log.debug("Pod template file found, will parse for base pod")
            pod_template = pod_generator.PodGenerator.deserialize_model_file(self.pod_template_file)
            if self.full_pod_spec:
                pod_template = PodGenerator.reconcile_pods(pod_template, self.full_pod_spec)
        elif self.full_pod_spec:
            pod_template = self.full_pod_spec
        else:
            pod_template = k8s.V1Pod(metadata=k8s.V1ObjectMeta())
        pod = k8s.V1Pod(
            api_version="v1",
            kind="Pod",
            metadata=k8s.V1ObjectMeta(
                namespace=self.namespace,
                labels=self.labels,
                name=self.name,
                annotations=self.annotations,
            ),
            spec=k8s.V1PodSpec(
                node_selector=self.node_selector,
                affinity=self.affinity,
                tolerations=self.tolerations,
                init_containers=self.init_containers,