**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
self.log.info("Starting the scheduler")
        
        pickle_dags = self.do_pickle and self.executor_class not in UNPICKLEABLE_EXECUTORS
        self.log.info("Processing each file at most %s times", self.num_times_parse_dags)
        
        
        async_mode = not self.using_sqlite
        processor_timeout_seconds: int = conf.getint('core', 'dag_file_processor_timeout')
        processor_timeout = timedelta(seconds=processor_timeout_seconds)
        self.processor_agent = DagFileProcessorAgent(
            dag_directory=self.subdir,
            max_runs=self.num_times_parse_dags,
            processor_factory=type(self)._create_dag_file_processor,
            processor_timeout=processor_timeout,
            dag_ids=[],
            pickle_dags=pickle_dags,
            async_mode=async_mode,
        )
        try:
            self.executor.job_id = self.id
            self.executor.start()