This is not possible to pass through to the filter node?

Not in any way that I can see, the filter node has a pretty straightforward constructor:
explicit FilterNodeOptions(Expression filter_expression, bool async_mode = true), it only accepts an expression.

I think that if you care about special handling nulls, you probably want to build an expression that evaluates as you wish for nulls

I think that if you care about special handling nulls, you probably want to build an expression that evaluates as you wish for nulls

I don't think is possible to get the "emit null" behaviour by changing the expression (for dropping/keeping, you can explicitly fill the null with False/True, but for preserving the row as null, that's only possible through this option). I suppose that is a good reason this is an option of the filter kernel and not eg comparison kernels.

Anyway, this is not that important given that the "drop" behaviour is the default for both (and is the typical behaviour you want, I think), but this might be something to open a JIRA for to add FilterOptions to the FilterNodeOptions (cc @westonpace would that make sense?)

Uhm, not sure I follow, why you can't use an expression?
Given

>>> pa.table({"rows": [1, 2, 3, None, 5, 6]})
pyarrow.Table
rows: int64
----
rows: [[1,2,3,null,5,6]]

If I want to drop the nulls, I do

>>> t.filter(pc.field("rows") < 5)
pyarrow.Table
rows: int64
----
rows: [[1,2,3]]

If instead I want to keep the nulls, I do

>>> t.filter((pc.field("rows") < 5) | (pc.field("rows").is_null()))
pyarrow.Table
rows: int64
----
rows: [[1,2,3,null]]

Regarding the "nulls" in the selection mask itself, I don't think FilterNode supports anything different from a boolean Expression, so the option doesn't make much sense in that context.

The option is about introducing nulls in the output data where the mask is null, not about preserving nulls from the input data. So for preserving nulls in the input, you can change your expression. But for introducing nulls, I don't think that is possible.

Using your example table:

In [29]: t.filter(pa.array([True, None, True, False, False, False]))
Out[29]: 
pyarrow.Table
rows: int64
----
rows: [[1,3]]

vs

In [33]: t.filter(pa.array([True, None, True, False, False, False]), null_selection_behavior="emit_null")
Out[33]: 
pyarrow.Table
rows: int64
----
rows: [[1,null,3]]

The null is in a place where the original data had a "2"