glm(binomial) vs. logistf

Dear friends,

Is there any reason why to run logistic regression (binomial response)
by glm() and not by logistf() by default? In particular when having
sparse data (e.g. 8 presences in 100  samples), frequently with
quasi-separation (all presences at one level of the predictor, together
with many absences).

I tried to read some papers by G. Heinze - I did not get the whole
thing, but it seems to me that both terms estimation and testing
procedure should be more reliable using logistf(). Am I wrong? 

So, is there any reason why to use binomial glm?
I am sorry for my ignorance - there should be a reason why people stick
to glm() - I just do not know what it is. Could you explain it to me or
point me to something to read, please? I am not a statistician by
training, however.

Thank you for your patience.

Kind regards,
Martin W.

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