### Apache Airflow version

2.2.1 (latest released)

### Operating System

PRETTY_NAME="Debian GNU/Linux 10 (buster)

### Versions of Apache Airflow Providers

apache-airflow-providers-ftp==2.0.1
apache-airflow-providers-http==2.0.1
apache-airflow-providers-imap==2.0.1
apache-airflow-providers-sqlite==2.0.1

### Deployment

Docker-Compose

### Deployment details

_No response_

### What happened

Backfilling via catchup=True leads to missing DagRuns. 

See reproduction steps for full details

### What you expected to happen

_No response_

### How to reproduce

Note, this is an issue which we have experienced in our production environment, with a much more complicated DAG. Below are the reproduction steps using breeze.

1. Setup ./breeze environment with the below config modifications
2. Create a simple DAG, with dummy tasks in it (see below example)
2. Set a `start_date` in the past
3. Set `catchup=True`
4. Unpause the DAG
5. Catch up starts and if you view the tree view, you have the false impression that everything has caught up correctly. 
6. However, access the calendar view, you can see the missing DagRuns. 

**Breeze Config**
```
export DB_RESET="true"
export START_AIRFLOW="true"
export INSTALL_AIRFLOW_VERSION="2.2.1"
export USE_AIRFLOW_VERSION="2.2.1"
```

**Dummy DAG**
```
from datetime import datetime
from airflow import DAG
from airflow.operators.dummy import DummyOperator

dag = DAG(
    dag_id="temp_test",
    schedule_interval="@daily",
    catchup=True,
    start_date=datetime(2021, 8, 1),
    max_active_tasks=10,
    max_active_runs=5,
    is_paused_upon_creation=True,
)

with dag:
    task1 = DummyOperator(task_id="task1")
    task2 = DummyOperator(task_id="task2")
    task3 = DummyOperator(task_id="task3")
    task4 = DummyOperator(task_id="task4")
    task5 = DummyOperator(task_id="task5")

    task1 >> task2 >> task3 >> task4 >> task5
```

**Results**
<img width="1430" alt="tree_view" src="https://user-images.githubusercontent.com/10559757/140715465-6bc3831c-d71c-4025-bcde-985010ab31f8.png">

<img width="1435" alt="calendar_view" src="https://user-images.githubusercontent.com/10559757/140715467-1a1a5c9a-3eb6-40ff-8720-ebe6db999028.png">


### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

airflow/models/dag.py
:type description: str
    :param schedule_interval: Defines how often that DAG runs, this
        timedelta object gets added to your latest task instance's
        execution_date to figure out the next schedule
    :type schedule_interval: datetime.timedelta or
        dateutil.relativedelta.relativedelta or str that acts as a cron
        expression
    :param timetable: Specify which timetable to use (in which case schedule_interval
        must not be set). See :doc:`/howto/timetable` for more information
    :type timetable: airflow.timetables.base.Timetable
    :param start_date: The timestamp from which the scheduler will
        attempt to backfill
    :type start_date: datetime.datetime
    :param end_date: A date beyond which your DAG won't run, leave to None
        for open ended scheduling
    :type end_date: datetime.datetime
    :param template_searchpath: This list of folders (non relative)
        defines where jinja will look for your templates. Order matters.
        Note that jinja/airflow includes the path of your DAG file by
        default
    :type template_searchpath: str or list[str]
    :param template_undefined: Template undefined type.
    :type template_undefined: jinja2.StrictUndefined
    :param user_defined_macros: a dictionary of macros that will be exposed
        in your jinja templates. For example, passing ``dict(foo='bar')``
        to this argument allows you to ``{{ foo }}`` in all jinja
        templates related to this DAG. Note that you can pass any
        type of object here.
    :type user_defined_macros: dict
    :param user_defined_filters: a dictionary of filters that will be exposed