**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

airflow/jobs/scheduler_job.py
return executable_tis
        
        task_instance_str = "\n\t".join([repr(x) for x in task_instances_to_examine])
        self.log.info("%s tasks up for execution:\n\t%s", len(task_instances_to_examine), task_instance_str)
        pool_to_task_instances: DefaultDict[str, List[models.Pool]] = defaultdict(list)
        for task_instance in task_instances_to_examine:
            pool_to_task_instances[task_instance.pool].append(task_instance)
        
        dag_concurrency_map: DefaultDict[str, int]
        task_concurrency_map: DefaultDict[Tuple[str, str], int]
        dag_concurrency_map, task_concurrency_map = self.__get_concurrency_maps(
            states=list(EXECUTION_STATES), session=session
        )
        num_tasks_in_executor = 0
        
        num_starving_tasks_total = 0
        
        
        
        for pool, task_instances in pool_to_task_instances.items():
            pool_name = pool
            if pool not in pools:
                self.log.warning("Tasks using non-existent pool '%s' will not be scheduled", pool)
                continue
            open_slots = pools[pool]["open"]
            num_ready = len(task_instances)
            self.log.info(
                "Figuring out tasks to run in Pool(name=%s) with %s open slots "
                "and %s task instances ready to be queued",
                pool,