**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/jobs/scheduler_job.py
unfinished_task_instances = (
                session.query(TI)
                .filter(TI.dag_id == dag_run.dag_id)
                .filter(TI.execution_date == dag_run.execution_date)
                .filter(TI.state.in_(State.unfinished))
            )
            for task_instance in unfinished_task_instances:
                task_instance.state = State.SKIPPED
                session.merge(task_instance)
            session.flush()
            self.log.info("Run %s of %s has timed-out", dag_run.run_id, dag_run.dag_id)
            
            self._update_dag_next_dagruns([session.query(DagModel).get(dag_run.dag_id)], session)
            callback_to_execute = DagCallbackRequest(
                full_filepath=dag.fileloc,
                dag_id=dag.dag_id,
                execution_date=dag_run.execution_date,
                is_failure_callback=True,
                msg='timed_out',
            )
            
            self._send_dag_callbacks_to_processor(dag_run, callback_to_execute)
            return 0
        if dag_run.execution_date > timezone.utcnow() and not dag.allow_future_exec_dates:
            self.log.error("Execution date is in future: %s", dag_run.execution_date)
            return 0
        if dag.max_active_runs:
            if (
                len(currently_active_runs) >= dag.max_active_runs
                and dag_run.execution_date not in currently_active_runs