The differences are likely due to the different approaches functions gamm4 and gamm use to approximate the likelihood.
(RE)ML estimation of GLMMs requires integrating the random effects out of the model likelihood. There is is no closed-form solution or ways to solve this analytically, so numerical methods must be used to approximate the integrals.
From the package documentation of function gamm4::gamm4: "gamm4 is based on gamm from package mgcv, but uses lme4 rather than nlme as the underlying fitting engine via a trick due to Fabian Scheipl. gamm4 is more robust numerically than gamm, and by avoiding PQL gives better performance for binary and low mean count data."
Dimitris Rizopoulos gives a great explanation of PQL and the different ways to numerically approximate the integrals: https://stats.stackexchange.com/a/436711/173546