Change tdigest centroids number for better accuracy

**Is your feature request related to a problem or challenge? Please describe what you are trying to do.**
We have a lot of use in approx_percentile in our product. which base on `tdigest`, we facing large amount data we face the issue which loss a lot accuracy.
In the paper, centroids number means how many sample data in your `tdigest`, so i think we should increase centroids number default size.

like in clickhouse set to 2048

https://github.com/ClickHouse/ClickHouse/blob/927af1255adb37ace1b95cc3ec4316553b4cb4b4/src/AggregateFunctions/QuantileTDigest.h#L71-L87

in spark and hive set to 10000 . https://github.com/apache/hive/blob/7b3ecf617a6d46f48a3b6f77e0339fd4ad95a420/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDAFPercentileApprox.java#L58-L59

**Describe the solution you'd like**
Change DEFAULT_MAX_SIZE to 10000

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context or screenshots about the feature request here.

Search the Rust source for DEFAULT_MAX_SIZE and trace how it is used by approx_percentile and the tdigest implementation. Check the surrounding aggregation tests or add coverage for the requested centroid limit, then confirm that the new default improves accuracy without unacceptable regressions.

Written by the indexing model from the issue text.