Support extracting Float{16, 32, 64} statistics from Parquet Data Pages

[alamb]

Is your feature request related to a problem or challenge?

Part of #10922

We are adding APIs to efficiently convert the data stored in Parquet's "PageIndex" into ArrayRefs -- which will make it significiantly easier to use this information for pruning and other tasks.

Describe the solution you'd like

Add support to StatisticsConverter::min_page_statistics and StatisticsConverter::max_page_statistics for the types above

datafusion/datafusion/core/src/datasource/physical_plan/parquet/statistics.rs

Lines 637 to 656 in a923c65

	/// of parquet page [`Index`]'es to an [`ArrayRef`]
	pub(crate) fn min_page_statistics<'a, I>(
	data_type: Option<&DataType>,
	iterator: I,
	) -> Result<ArrayRef>
	where
	I: Iterator<Item = (usize, &'a Index)>,
	{
	get_data_page_statistics!(Min, data_type, iterator)
	}
	/// Extracts the max statistics from an iterator
	/// of parquet page [`Index`]'es to an [`ArrayRef`]
	pub(crate) fn max_page_statistics<'a, I>(
	data_type: Option<&DataType>,
	iterator: I,
	) -> Result<ArrayRef>
	where
	I: Iterator<Item = (usize, &'a Index)>,
	{

Describe alternatives you've considered

You can follow the model from @Weijun-H in #10931

1. Update the test for the listed data types following the model of test_int64

   datafusion/datafusion/core/tests/parquet/arrow_statistics.rs

   Lines 506 to 529 in a923c65

   	async fn test_int_64() {
   	// This creates a parquet files of 4 columns named "i8", "i16", "i32", "i64"
   	let reader = TestReader {
   	scenario: Scenario::Int,
   	row_per_group: 5,
   	}
   	.build()
   	.await;
   	// since each row has only one data page, the statistics are the same
   	Test {
   	reader: &reader,
   	// mins are [-5, -4, 0, 5]
   	expected_min: Arc::new(Int64Array::from(vec![-5, -4, 0, 5])),
   	// maxes are [-1, 0, 4, 9]
   	expected_max: Arc::new(Int64Array::from(vec![-1, 0, 4, 9])),
   	// nulls are [0, 0, 0, 0]
   	expected_null_counts: UInt64Array::from(vec![0, 0, 0, 0]),
   	// row counts are [5, 5, 5, 5]
   	expected_row_counts: UInt64Array::from(vec![5, 5, 5, 5]),
   	column_name: "i64",
   	check: Check::Both,
   	}
   	.run();

2. Add any required implementation in

   datafusion/datafusion/core/src/datasource/physical_plan/parquet/statistics.rs

   Lines 575 to 586 in 2f43476

   	make_data_page_stats_iterator!(MinInt64DataPageStatsIterator, min, Index::INT64, i64);
   	make_data_page_stats_iterator!(MaxInt64DataPageStatsIterator, max, Index::INT64, i64);
   	macro_rules! get_data_page_statistics {
   	($stat_type_prefix: ident, $data_type: ident, $iterator: ident) => {
   	paste! {
   	match $data_type {
   	Some(DataType::Int64) => Ok(Arc::new(Int64Array::from_iter([<$stat_type_prefix Int64DataPageStatsIterator>]::new($iterator).flatten()))),
   	_ => unimplemented!()
   	}
   	}
   	}

   (follow the model of the row counts,

   datafusion/datafusion/core/src/datasource/physical_plan/parquet/statistics.rs

   Line 90 in 2f43476

   macro_rules! make_stats_iterator {

   )

Additional context

No response