```json
{
  "sym_variables": [
    ("x0", "chicken thighs"),
    ("x1", "potatoes"),
    ("x2", "tomatoes"),
    ("x3", "knishes"),
    ("x4", "bananas"),
    ("x5", "green beans")
  ],
  "objective_function": "1.36*x0**2 + 4.33*x0*x1 + 9.9*x1*x2 + 9.03*x2*x4 + 8.22*x2*x5 + 7.37*x3*x5 + 6.95*x4*x5 + 1.11*x0 + 6.63*x1 + 2.13*x2 + 2.25*x3",
  "constraints": [
    "8.49*x0**2 + 13.22*x2**2 >= 54",
    "13.92*x2 + 11.57*x3 + 0.58*x4 >= 52",
    "12.04*x0 + 13.92*x2 + 6.75*x5 >= 52",
    "11.57*x3 + 0.58*x4 + 6.75*x5 >= 52",
    "12.04*x0 + 13.92*x2 + 11.57*x3 >= 52",
    "12.04*x0 + 8.45*x1 + 0.58*x4 >= 52",
    "12.04*x0 + 13.92*x2 + 0.58*x4 >= 52",
    "8.45*x1 + 11.57*x3 + 0.58*x4 >= 52",
    "13.92*x2 + 11.57*x3 + 0.58*x4 >= 45",
    "12.04*x0 + 13.92*x2 + 6.75*x5 >= 45",
    "11.57*x3 + 0.58*x4 + 6.75*x5 >= 45",
    "12.04*x0 + 13.92*x2 + 11.57*x3 >= 45",
    "12.04*x0**2 + 8.45*x1**2 + 0.58*x4**2 >= 45",
    "12.04*x0**2 + 13.92*x2**2 + 0.58*x4**2 >= 45",
    "8.45*x1 + 11.57*x3 + 0.58*x4 >= 45",
    "13.92*x2 + 11.57*x3 + 0.58*x4 >= 53",
    "12.04*x0 + 13.92*x2 + 6.75*x5 >= 53",
    "11.57*x3**2 + 0.58*x4**2 + 6.75*x5**2 >= 53",
    "12.04*x0**2 + 13.92*x2**2 + 11.57*x3**2 >= 53",
    "12.04*x0 + 8.45*x1 + 0.58*x4 >= 53",
    "12.04*x0 + 13.92*x2 + 0.58*x4 >= 53",
    "8.45*x1 + 11.57*x3 + 0.58*x4 >= 53",
    "13.92*x2 + 11.57*x3 + 0.58*x4 >= 51",
    "12.04*x0 + 13.92*x2 + 6.75*x5 >= 51",
    "11.57*x3**2 + 0.58*x4**2 + 6.75*x5**2 >= 51",
    "12.04*x0**2 + 13.92*x2**2 + 11.57*x3**2 >= 51",
    "12.04*x0 + 8.45*x1 + 0.58*x4 >= 51",
    "12.04*x0 + 13.92*x2 + 0.58*x4 >= 51",
    "8.45*x1 + 11.57*x3 + 0.58*x4 >= 51",
    "8.49*x0 <= 343",
    "12.04*x0 <= 420",
    "11.84*x0 <= 270",
    "6.78*x1 <= 343",
    "8.45*x1 <= 420",
    "1.74*x1 <= 270",
    "13.22*x2 <= 343",
    "13.92*x2 <= 420",
    "8.63*x2 <= 270",
    "1.16*x3 <= 343",
    "11.57*x3 <= 420",
    "0.85*x3 <= 270",
    "9.54*x4 <= 343",
    "0.58*x4 <= 420",
    "3.88*x4 <= 270",
    "14.58*x5 <= 343",
    "6.75*x5 <= 420",
    "12.18*x5 <= 270"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
chicken_thighs = m.addVar(lb=0, name="chicken_thighs")
potatoes = m.addVar(lb=0, name="potatoes")
tomatoes = m.addVar(lb=0, name="tomatoes")
knishes = m.addVar(lb=0, name="knishes")
bananas = m.addVar(lb=0, name="bananas")
green_beans = m.addVar(lb=0, name="green_beans")


# Set objective function
m.setObjective(1.36*chicken_thighs**2 + 4.33*chicken_thighs*potatoes + 9.9*potatoes*tomatoes + 9.03*tomatoes*bananas + 8.22*tomatoes*green_beans + 7.37*knishes*green_beans + 6.95*bananas*green_beans + 1.11*chicken_thighs + 6.63*potatoes + 2.13*tomatoes + 2.25*knishes, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(8.49*chicken_thighs**2 + 13.22*tomatoes**2 >= 54)
m.addConstr(13.92*tomatoes + 11.57*knishes + 0.58*bananas >= 52)
# ... (add all other constraints similarly)

# Resource constraints
m.addConstr(8.49 * chicken_thighs <= 343)
m.addConstr(12.04 * chicken_thighs <= 420)
m.addConstr(11.84 * chicken_thighs <= 270)
# ... (add resource constraints for other ingredients)


# 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)

```