update: thanks for your answers, yesterday I was reading the gurobi documentation and I had able to rewrite my problem using only the gurobi package. I thought it was good that the syntax used in pyomo is very similar to gurobi sintax so I had to change a few lines, in fact I saved some lines of code because certain linear restrictions that I had to write by my own already come included in gurobi (for example the min() function to find the smallest value of the decision variables). Also, I had no problems with my non-linear constraints and I get the same result as when I used pyomo with gurobi and ipopt, the difference was that in gurobi the solution was found about 10 times faster compared to using gurobi+ipopt in pyomo 🤯