This is interesting but leads to tricky use cases. Looking at the logic of automated analysis:  

First, any piece of land should in principle have some sort of landuse attached, even by default implicitly or explicitly stating unused.  If the landuse for this small region is not religious, what is it? 

Further, a car parked in that parking lot is engaged in religious activity.  It is a private parking lot owned by the religious entity associated with the place of worship.  The attributes for the parking lot, were tagging complete, might indicate it is not public and for the use of the facility attached.  A land use polygon of course encodes  that naturally avoid the entire issue of parsing a parking lot tagset. 

Assume I have a billion accurate lat-longs and I want to find which of them are attributable to religious entities.  I would want any falling into the church grounds, especially the parking, to come up with a religious tag. 


Inside the US I can do this with land parcel data, but that is messy compared to just having broadly implemented landuse=religious tagging scheme. 


I am not disputing your assertion or the practices in the wiki and will avoid edits contravening that. I am noting that this practice leads to inconsistencies when parsing the model of the world. 


 