**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

airflow/executors/kubernetes_executor.py
'dag_id': annotations['dag_id'],
                'task_id': annotations['task_id'],
                'execution_date': annotations['execution_date'],
                'try_number': annotations['try_number'],
            }
            self.process_status(
                pod_id=task.metadata.name,
                namespace=task.metadata.namespace,
                status=task.status.phase,
                annotations=task_instance_related_annotations,
                resource_version=task.metadata.resource_version,
                event=event,
            )
            last_resource_version = task.metadata.resource_version
        return last_resource_version
    def process_error(self, event: Any) -> str:
        """Process error response"""
        self.log.error('Encountered Error response from k8s list namespaced pod stream => %s', event)
        raw_object = event['raw_object']
        if raw_object['code'] == 410:
            self.log.info(
                'Kubernetes resource version is too old, must reset to 0 => %s', (raw_object['message'],)
            )
            
            return '0'
        raise AirflowException(
            'Kubernetes failure for %s with code %s and message: %s'
            % (raw_object['reason'], raw_object['code'], raw_object['message'])
        )
    def process_status(