fix(amber): stop JSONL scan reading null as text - #8740

…ython (apache#8704)

### What changes were proposed in this PR?

The JSON Lines and Arrow sources implement `StandaloneCodeGenerator`, so
an exported script starts from the same data the workflow did.

Arrow is read into pandas' nullable dtypes rather than its numpy ones.
Arrow says of every value whether it is there, and a numpy column has
nowhere to put that: a missing double and a stored NaN both land on NaN,
and a missing integer costs the column its integer type. The engine
reads the file's own answer, so a set operation keeps a NaN row a null
row does not match, a holed integer column is still made of integers,
and a long past 2^53 is not rounded on its way through a float. The
other sources state the types they need one at a time, their formats
having no way to say it once.

A file also states the width and the sign of each of its numbers, and
Texera has no column narrower than a double or a 32-bit integer. A
single-precision or 16-bit column was arriving null, the parse having no
case for the Float and Short those vectors hand back, and an unsigned
one arrived as the -1 its storage counts down to. Each now reads as the
Texera type that holds it, an unsigned 32-bit column as a long and an
unsigned 64-bit one refused for want of a wider one, and the script
normalizes the same widths so a later sum comes to the same number on
both sides. This is the rule the Parquet source already follows. The end
of an exported Arrow window is counted in Long, as the CSV readers count
theirs.

A timestamp is read as the engine reads one. The engine hands each field
to DateParserUtils, so a row states its own format, any year reads, an
offset is moved to the local zone, and the reading is cut to the
millisecond. pandas' date parsing does none of that. So JSONL reads the
column as text and converts it with the helper apache#8597 adds.

This PR also changes the engine. `ArrowUtils` handed a TIMESTAMP column
to a Python worker as an Arrow millisecond timestamp labelled UTC. A
Python operator saw an aware datetime cut to the millisecond, and a year
past 2262 overflowed. It is now a zoneless microsecond timestamp. That
is what Texera's own Python side declares for a TIMESTAMP
(`pa.timestamp("us")`), and what an exported script holds. Two things a
user can see change. A Python UDF now receives a naive datetime instead
of one in UTC. A result downloaded as an Arrow file now carries a
zoneless microsecond timestamp column instead of a UTC millisecond one.
A TIMESTAMP parameter a Python UDF declares is now parsed the same way,
as a naive wall clock with any offset moved to the machine's zone, so it
compares with a row's instead of raising.

### Any related issues, documentation, discussions?

Part of apache#8325, step 12b; that issue lists the set in order. It uses
apache#8341's shared source code and apache#8597's timestamp helpers.

Its JSON Lines export reads a JSON `null` as the text `"null"`, as the
engine does today (apache#8674). If apache#8740 merges first, that part changes to
read a null before this one merges.

Closes apache#8702, the task this change is the whole of.

It also finishes apache#8602, where a scan window of no rows left the type
inference nothing to read and JSONL declared a schema of no columns.
apache#8341 fixes the CSV half.

Closes apache#8602.

### How was this PR tested?

Each operator asserts the block it emits in its own spec, and the Arrow
spec runs the engine's own executor and the generated script over the
same file. The engine change is covered in both ArrowUtilsSpec files,
which include a round trip on either side of the nanosecond range, and
in the Python tests of the timestamp parser. Once the verification
lands, every operator is also run both ways on every configuration its
schema offers; this branch is cut from main and does not carry that
machinery, so those runs are not on this diff's CI.

Arrow is the one worth calling out. Its column of timestamps only
matches once apache#7672 is in: before that the engine read the file's numbers
through the JVM's zone while `pd.read_feather` read the wall clock the
file states, and the two differed by whatever the machine was set to.
That change has since landed.

### Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Opus 5)

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Co-authored-by: Claude Opus 5.5 (1M context) <[email protected]>