### Apache Airflow version

2.4.1

### What happened

When using dataset scheduling, it isn't obvious which datasets a downstream dataset consumer is awaiting in order for the DAG to be scheduled.

I would assume that this is supposed to be solved by the `Latest Update` column in the modal that opens when selecting `x of y datasets updated`, but it appears that the data isn't being populated.

<img width="601" alt="image" src="https://user-images.githubusercontent.com/5778047/194116186-d582cede-c778-47f7-8341-fc13a69a2358.png">

Although one of the datasets has been produced, there is no data in the `Latest Update` column of the modal.

In the above example, both datasets have been produced > 1 time.

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116368-ceff241f-a623-4893-beb7-637b821c4b53.png">

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116410-19045f5a-8400-47b0-afcb-9fbbffca26ee.png">


### What you think should happen instead

The `Latest Update` column should be populated with the latest update timestamp for each dataset required to schedule a downstream, dataset consuming DAG.

Ideally there would be some form of highlighting on the "missing" datasets for quick visual feedback when DAGs have a large number of datasets required for scheduling.

### How to reproduce

1. Create a DAG (or 2 individual DAGs) that produces 2 datasets
2. Produce both datasets
3. Then produce _only one_ dataset
4. Check the modal by clicking from the home screen on the `x of y datasets updated` button.

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

_No response_

### Deployment

Docker-Compose

### Deployment details

_No response_

### 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)

filter(None, [record["start_date"], ti_summary.start_date]), default=None
                    )
                    record["end_date"] = max(
                        filter(None, [record["end_date"], ti_summary.end_date]), default=None
                    )
                    record["mapped_states"][ti_summary.state] = ti_summary.state_count
                if record:
                    set_overall_state(record)
                    yield record
            if isinstance(item, MappedOperator) or is_parent_mapped:
                instances = list(_mapped_summary(grouped_tis.get(item.task_id, [])))
            else:
                instances = list(map(_get_summary, grouped_tis.get(item.task_id, [])))
            return {
                "id": item.task_id,
                "instances": instances,
                "label": item.label,
                "extra_links": item.extra_links,
                "is_mapped": isinstance(item, MappedOperator) or is_parent_mapped,
                "has_outlet_datasets": any(isinstance(i, Dataset) for i in (item.outlets or [])),
                "operator": item.operator_name,
            }
        
        task_group = item
        group_is_mapped = isinstance(task_group, MappedTaskGroup)
        children = [
            task_group_to_grid(child, grouped_tis, is_parent_mapped=group_is_mapped)
            for child in task_group.topological_sort()
        ]
        def get_summary(dag_run: DagRun):