Full join on dataframe with only index yields dropped rows

[ntjohnson1]

Describe the bug
If I do a full join between a dataframe with content and one only consisting of an index column that index column only bits get dropped.

To Reproduce
See commented out empty additional column. When that is included then we see results in the final dataframe.

ctx = dfn.SessionContext()
    key_frame = ctx.from_pydict(
        {
            "log_time": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],
            "key_frame": [True, True, True, True, True, True, True, True, True, True]
        }
    )
    query_times = ctx.from_pydict(
        {
            "log_time": [2, 4, 6, 8, 10],
            #"empty": [0, 0, 0, 0, 0]
        }
    )

    print(key_frame)
    print(query_times)
    merged = query_times.join(key_frame, left_on="log_time", right_on="log_time", how="full")
    print(merged)

DataFrame()
+----------+-----------+
| log_time | key_frame |
+----------+-----------+
| 1        | true      |
| 3        | true      |
| 5        | true      |
| 7        | true      |
| 9        | true      |
| 11       | true      |
| 13       | true      |
| 15       | true      |
| 17       | true      |
| 19       | true      |
+----------+-----------+
DataFrame()
+----------+
| log_time |
+----------+
| 2        |
| 4        |
| 6        |
| 8        |
| 10       |
+----------+
DataFrame()
+----------+----------+-----------+
| log_time | log_time | key_frame |
+----------+----------+-----------+
|          | 1        | true      |
|          | 3        | true      |
|          | 5        | true      |
|          | 7        | true      |
|          | 9        | true      |
|          | 11       | true      |
|          | 13       | true      |
|          | 15       | true      |
|          | 17       | true      |
|          | 19       | true      |
+----------+----------+-----------+

Expected behavior
When doing a full join I get back all rows. Effectively merging the dataframes.

Here is a somewhat equivalent in pandas

key_frame_df = pd.DataFrame({
        "log_time": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],
        "key_frame": [True, True, True, True, True, True, True, True, True, True]
    })
    
    query_times_df = pd.DataFrame({
        "log_time": [2, 4, 6, 8, 10],
        # "empty": [0, 0, 0, 0, 0]  # commented out like in original
    })
    
    
    # Perform full outer join (equivalent to DataFusion's "full" join)
    merged_df = pd.merge(query_times_df, key_frame_df, on="log_time", how="outer")
    print("\nMerged DataFrame (full outer join):")
    print(merged_df)

Merged DataFrame (full outer join):
    log_time key_frame
0          1      True
1          2       NaN
2          3      True
3          4       NaN
4          5      True
5          6       NaN
6          7      True
7          8       NaN
8          9      True
9         10       NaN
10        11      True
11        13      True
12        15      True
13        17      True
14        19      True

Actually the behavior in pyarrow is maybe a more direct comparison

key_table = pa.table(key_frame)
  query_table = pa.table(query_times)
  merged_table = query_table.join(key_table, keys="log_time", join_type="full outer")
  print(ctx.from_arrow(merged_table))