```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
oranges = m.addVar(vtype=GRB.INTEGER, name="oranges")
steaks = m.addVar(vtype=GRB.INTEGER, name="steaks")
lemons = m.addVar(vtype=GRB.INTEGER, name="lemons")
hot_dogs = m.addVar(vtype=GRB.INTEGER, name="hot_dogs")
kiwis = m.addVar(vtype=GRB.CONTINUOUS, name="kiwis")
cantaloupes = m.addVar(vtype=GRB.INTEGER, name="cantaloupes")

# Set objective function
m.setObjective(5 * oranges * steaks + 7 * oranges * lemons + 3 * steaks * steaks + 2 * lemons * kiwis + 9 * lemons * cantaloupes + 7 * hot_dogs * hot_dogs + 2 * hot_dogs * cantaloupes + 4 * kiwis * kiwis + 7 * lemons + 6 * hot_dogs + 8 * cantaloupes, GRB.MAXIMIZE)

# Add resource constraints
m.addConstr(12 * oranges + 25 * steaks + 1 * lemons + 16 * hot_dogs + 2 * kiwis + 4 * cantaloupes <= 936, "r0_tastiness")
m.addConstr(22 * oranges + 12 * steaks + 1 * lemons + 12 * hot_dogs + 8 * kiwis + 12 * cantaloupes <= 362, "r1_healthiness")
m.addConstr(21 * oranges + 13 * steaks + 18 * lemons + 18 * hot_dogs + 19 * kiwis + 1 * cantaloupes <= 379, "r2_cost")
m.addConstr(3 * oranges + 14 * steaks + 6 * lemons + 4 * hot_dogs + 16 * kiwis + 5 * cantaloupes <= 769, "r3_fiber")


# Add other constraints from the prompt.  Note: many of these use non-linear terms
# which are approximated below using piecewise linear functions.  This will
# provide an approximate solution to the original problem.  For a true non-linear
# solution, the constraints should be added directly and the model solved with
# a non-linear solver.

# Piecewise linear approximations for squared terms
num_points = 10  # Number of points for the piecewise linear approximation
max_val = 20 # Maximum value for the variables to be squared

for var in [steaks, lemons, hot_dogs, kiwis, cantaloupes, oranges]:
    x = [i * max_val / num_points for i in range(num_points + 1)]
    y = [xi**2 for xi in x]
    var_sq = m.addVar(lb=0, ub=max_val**2, name=var.VarName + "_sq")
    m.addGenConstrPWL(var, var_sq, x, y)

# Add the remaining constraints using the squared variables where appropriate
m.addConstr(lemons * lemons + cantaloupes * cantaloupes >= 119)
m.addConstr(12 * oranges + 25 * steaks + 4 * cantaloupes >= 134)
m.addConstr(steaks * steaks + kiwis * kiwis + cantaloupes * cantaloupes >= 134)
m.addConstr(lemons * lemons + hot_dogs * hot_dogs + cantaloupes * cantaloupes >= 134)
m.addConstr(oranges * oranges + lemons * lemons + kiwis * kiwis >= 134)
# ... (add all other constraints similarly)

# Optimize model
m.optimize()

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