**Apache Airflow version**:
2.0.0

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
1.15
**Environment**:

- **Cloud provider or hardware configuration**: 
GKE
- **OS** (e.g. from /etc/os-release):
Ubuntu 18.04

**What happened**:
I just migrated from 1.10.14 to 2.0.0. When I turn on some random dags, the scheduler crashes with the following error:

```python
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1275, in _execute
    self._run_scheduler_loop()
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1377, in _run_scheduler_loop
    num_queued_tis = self._do_scheduling(session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1533, in _do_scheduling
    num_queued_tis = self._critical_section_execute_task_instances(session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1132, in _critical_section_execute_task_instances
    queued_tis = self._executable_task_instances_to_queued(max_tis, session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/utils/session.py", line 62, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1034, in _executable_task_instances_to_queued
    if task_instance.pool_slots > open_slots:
TypeError: '>' not supported between instances of 'NoneType' and 'int'
```
**What you expected to happen**:

I expected those dags would have their tasks scheduled without problems.

**How to reproduce it**:

Can't reproduce it yet. Still trying to figure out if this happens only with specific dags or not.

**Anything else we need to know**:

I couldn't find in which context `task_instance.pool_slots` could be None

airflow/models/taskinstance.py
return result.strftime(fmt)
        return ''
    @provide_session
    def get_template_context(self, session=None) -> Context:  
        """Return TI Context"""
        task = self.task
        from airflow import macros
        integrate_macros_plugins()
        params = {}  
        run_id = ''
        dag_run = None
        if hasattr(task, 'dag'):
            if task.dag.params:
                params.update(task.dag.params)
            from airflow.models.dagrun import DagRun  
            dag_run = (
                session.query(DagRun)
                .filter_by(dag_id=task.dag.dag_id, execution_date=self.execution_date)
                .first()
            )
            run_id = dag_run.run_id if dag_run else None
            session.expunge_all()
            session.commit()
        ds = self.execution_date.strftime('%Y-%m-%d')
        ts = self.execution_date.isoformat()
        yesterday_ds = (self.execution_date - timedelta(1)).strftime('%Y-%m-%d')
        tomorrow_ds = (self.execution_date + timedelta(1)).strftime('%Y-%m-%d')