Here's the Gurobi code that represents the optimization problem you described:

```python
import gurobipy as gp

# Create a new model
m = gp.Model("food_optimization")

# Create variables
knishes = m.addVar(lb=0, name="knishes")
ravioli = m.addVar(lb=0, name="ravioli")
cantaloupes = m.addVar(lb=0, name="cantaloupes")
chicken_thighs = m.addVar(lb=0, name="chicken_thighs")

# Set objective function
m.setObjective(3.6 * knishes**2 + 8.76 * knishes * ravioli + 8.77 * knishes * cantaloupes + 5.38 * knishes * chicken_thighs + 5.13 * cantaloupes**2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(10.29 * knishes**2 + 5.24 * cantaloupes**2 >= 24, "calcium_constraint1")
m.addConstr(5.24 * cantaloupes**2 + 4.29 * chicken_thighs**2 >= 29, "calcium_constraint2")
m.addConstr(10.29 * knishes + 4.29 * chicken_thighs >= 21, "calcium_constraint3")
m.addConstr(8.15 * ravioli + 5.24 * cantaloupes >= 30, "calcium_constraint4")
m.addConstr(10.29 * knishes + 8.15 * ravioli + 5.24 * cantaloupes + 4.29 * chicken_thighs >= 30, "calcium_constraint5")

m.addConstr(1.94 * knishes + 9.01 * cantaloupes >= 18, "fat_constraint1")
m.addConstr(8.63 * ravioli + 9.01 * cantaloupes >= 19, "fat_constraint2")
m.addConstr(1.94 * knishes + 8.63 * ravioli >= 13, "fat_constraint3")
m.addConstr(1.94 * knishes**2 + 4.03 * chicken_thighs**2 >= 20, "fat_constraint4")
m.addConstr(8.63 * ravioli + 9.01 * cantaloupes + 4.03 * chicken_thighs >= 20, "fat_constraint5")
m.addConstr(1.94 * knishes + 8.63 * ravioli + 9.01 * cantaloupes + 4.03 * chicken_thighs >= 20, "fat_constraint6")


m.addConstr(-3 * knishes + 2 * ravioli >= 0, "constraint7")
m.addConstr(5.24 * cantaloupes + 4.29 * chicken_thighs <= 63, "calcium_constraint6")
m.addConstr(10.29 * knishes + 4.29 * chicken_thighs <= 72, "calcium_constraint7")
m.addConstr(8.63 * ravioli**2 + 4.03 * chicken_thighs**2 <= 42, "fat_constraint7")
m.addConstr(1.94 * knishes + 8.63 * ravioli + 4.03 * chicken_thighs <= 74, "fat_constraint8")
m.addConstr(1.94 * knishes + 8.63 * ravioli + 9.01 * cantaloupes <= 62, "fat_constraint9")
m.addConstr(1.94 * knishes + 9.01 * cantaloupes + 4.03 * chicken_thighs <= 45, "fat_constraint10")


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```
