feat: add to_sedonadb() method

It would be nice to have an interface that converts a SedonaSpark DataFrame to a SedonaDB DataFrame easily. Here is a current solution that works:

```python
import sedona.db
sd = sedona.db.connect()

df = sd.create_data_frame(dataframe_to_arrow(spark_df))
```

This could be nice:

```python
spark_df.to_sedonadb()
```

But maybe we'd have to do this:

```python
spark_df.to_sedonadb(sd)
```

This would allow for cool spatial workflows, like this:

* Read an Iceberg table with SedonaSpark and perform big data operations with a filtering operation at the end to make the data small enough to fit on a single machine
* Convert the SedonaSpark DataFrame to SedonaDB
* Use a library that's compatible with SedonaDB, like lonboard, to create a graph

Let me know what you think!

Start by reviewing the current conversion path shown in the issue: dataframe_to_arrow(spark_df), sedona.db.connect(), and create_data_frame(). Decide whether the interface should accept a SedonaDB connection or create one implicitly; done means a documented to_sedonadb() workflow supports the proposed SedonaSpark-to-SedonaDB conversion.

Written by the indexing model from the issue text.