**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
open_slots,
                num_ready,
            )
            priority_sorted_task_instances = sorted(
                task_instances, key=lambda ti: (-ti.priority_weight, ti.execution_date)
            )
            num_starving_tasks = 0
            for current_index, task_instance in enumerate(priority_sorted_task_instances):
                if open_slots <= 0:
                    self.log.info("Not scheduling since there are %s open slots in pool %s", open_slots, pool)
                    
                    num_unhandled = len(priority_sorted_task_instances) - current_index
                    num_starving_tasks += num_unhandled
                    num_starving_tasks_total += num_unhandled
                    break
                
                
                dag_id = task_instance.dag_id
                current_dag_concurrency = dag_concurrency_map[dag_id]
                dag_concurrency_limit = task_instance.dag_model.concurrency
                self.log.info(
                    "DAG %s has %s/%s running and queued tasks",
                    dag_id,
                    current_dag_concurrency,
                    dag_concurrency_limit,
                )
                if current_dag_concurrency >= dag_concurrency_limit:
                    self.log.info(
                        "Not executing %s since the number of tasks running or queued "
                        "from DAG %s is >= to the DAG's task concurrency limit of %s",