```json
{
  "sym_variables": [
    ("x0", "black beans"),
    ("x1", "bananas"),
    ("x2", "corn cobs"),
    ("x3", "hamburgers")
  ],
  "objective_function": "7*x0 + 8*x1 + 4*x2 + 7*x3",
  "constraints": [
    "5.97*x1 + 9.6*x2 >= 24",
    "10.38*x0 + 9.6*x2 >= 16",
    "10.38*x0 + 5.97*x1 + 1.28*x3 >= 25",
    "10.38*x0 + 5.97*x1 + 9.6*x2 + 1.28*x3 >= 25",
    "8.04*x0 + 3.8*x1 >= 23",
    "8.04*x0 + 9.58*x3 >= 39",
    "8.04*x0 + 7.77*x2 >= 23",
    "8.04*x0 + 3.8*x1 + 9.58*x3 >= 28",
    "8.04*x0 + 7.77*x2 + 9.58*x3 >= 28",
    "8.04*x0 + 3.8*x1 + 9.58*x3 >= 32",
    "8.04*x0 + 7.77*x2 + 9.58*x3 >= 32",
    "8.04*x0 + 3.8*x1 + 7.77*x2 + 9.58*x3 >= 32",
    "7.25*x1 + 1.68*x2 >= 29",
    "2.96*x0 + 9.9*x3 >= 30",
    "2.96*x0 + 1.68*x2 >= 19",
    "2.96*x0 + 7.25*x1 + 1.68*x2 >= 30",
    "2.96*x0 + 7.25*x1 + 1.68*x2 + 9.9*x3 >= 30",
    "4.45*x1 + 5.45*x2 >= 23",
    "8.47*x0 + 5.45*x2 >= 20",
    "8.47*x0 + 0.76*x3 >= 24",
    "5.45*x2 + 0.76*x3 >= 36",
    "8.47*x0 + 4.45*x1 + 0.76*x3 >= 28",
    "8.47*x0 + 4.45*x1 + 5.45*x2 + 0.76*x3 >= 28",
    "8.75*x0 + 5.16*x1 >= 29",
    "8.75*x0 + 5.16*x1 + 2.4*x2 + 8.76*x3 >= 29",
    "1*x1 - 2*x3 >= 0",
    "3*x0 - 10*x2 >= 0",
    "3*x2 - 10*x3 >= 0",
    "3.8*x1 + 7.77*x2 <= 55",
    "8.04*x0 + 9.58*x3 <= 84",
    "7.77*x2 + 9.58*x3 <= 123",
    "3.8*x1 + 9.58*x3 <= 154",
    "3.8*x1 + 7.77*x2 + 9.58*x3 <= 172",
    "8.04*x0 + 3.8*x1 + 9.58*x3 <= 69",
    "8.04*x0 + 7.77*x2 + 9.58*x3 <= 104",
    "2.96*x0 + 9.9*x3 <= 48",
    "2.96*x0 + 7.25*x1 <= 163",
    "7.25*x1 + 1.68*x2 <= 88",
    "2.96*x0 + 1.68*x2 <= 80",
    "1.68*x2 + 9.9*x3 <= 137",
    "7.25*x1 + 9.9*x3 <= 62",
    "8.47*x0 + 5.45*x2 + 0.76*x3 <= 151",
    "8.75*x0 + 8.76*x3 <= 213",
    "5.16*x1 + 8.76*x3 <= 101",
    "5.16*x1 + 2.4*x2 <= 180",
    "8.75*x0 + 5.16*x1 + 8.76*x3 <= 75"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
black_beans = m.addVar(lb=0, name="black_beans")
bananas = m.addVar(lb=0, name="bananas")
corn_cobs = m.addVar(lb=0, name="corn_cobs")
hamburgers = m.addVar(lb=0, name="hamburgers")


# Set objective function
m.setObjective(7 * black_beans + 8 * bananas + 4 * corn_cobs + 7 * hamburgers, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(5.97 * bananas + 9.6 * corn_cobs >= 24)
m.addConstr(10.38 * black_beans + 9.6 * corn_cobs >= 16)
m.addConstr(10.38 * black_beans + 5.97 * bananas + 1.28 * hamburgers >= 25)
m.addConstr(10.38 * black_beans + 5.97 * bananas + 9.6 * corn_cobs + 1.28 * hamburgers >= 25)
m.addConstr(8.04 * black_beans + 3.8 * bananas >= 23)
m.addConstr(8.04 * black_beans + 9.58 * hamburgers >= 39)
m.addConstr(8.04 * black_beans + 7.77 * corn_cobs >= 23)
m.addConstr(8.04 * black_beans + 3.8 * bananas + 9.58 * hamburgers >= 28)
m.addConstr(8.04 * black_beans + 7.77 * corn_cobs + 9.58 * hamburgers >= 28)
m.addConstr(8.04 * black_beans + 3.8 * bananas + 9.58 * hamburgers >= 32)
m.addConstr(8.04 * black_beans + 7.77 * corn_cobs + 9.58 * hamburgers >= 32)
m.addConstr(8.04 * black_beans + 3.8 * bananas + 7.77 * corn_cobs + 9.58 * hamburgers >= 32)
m.addConstr(7.25 * bananas + 1.68 * corn_cobs >= 29)
m.addConstr(2.96 * black_beans + 9.9 * hamburgers >= 30)
m.addConstr(2.96 * black_beans + 1.68 * corn_cobs >= 19)
m.addConstr(2.96 * black_beans + 7.25 * bananas + 1.68 * corn_cobs >= 30)
m.addConstr(2.96 * black_beans + 7.25 * bananas + 1.68 * corn_cobs + 9.9 * hamburgers >= 30)
m.addConstr(4.45 * bananas + 5.45 * corn_cobs >= 23)
m.addConstr(8.47 * black_beans + 5.45 * corn_cobs >= 20)
m.addConstr(8.47 * black_beans + 0.76 * hamburgers >= 24)
m.addConstr(5.45 * corn_cobs + 0.76 * hamburgers >= 36)
m.addConstr(8.47 * black_beans + 4.45 * bananas + 0.76 * hamburgers >= 28)
m.addConstr(8.47 * black_beans + 4.45 * bananas + 5.45 * corn_cobs + 0.76 * hamburgers >= 28)
m.addConstr(8.75 * black_beans + 5.16 * bananas >= 29)
m.addConstr(8.75 * black_beans + 5.16 * bananas + 2.4 * corn_cobs + 8.76 * hamburgers >= 29)
m.addConstr(1 * bananas - 2 * hamburgers >= 0)
m.addConstr(3 * black_beans - 10 * corn_cobs >= 0)
m.addConstr(3 * corn_cobs - 10 * hamburgers >= 0)
m.addConstr(3.8 * bananas + 7.77 * corn_cobs <= 55)
m.addConstr(8.04 * black_beans + 9.58 * hamburgers <= 84)
m.addConstr(7.77 * corn_cobs + 9.58 * hamburgers <= 123)
m.addConstr(3.8 * bananas + 9.58 * hamburgers <= 154)
m.addConstr(3.8 * bananas + 7.77 * corn_cobs + 9.58 * hamburgers <= 172)
m.addConstr(8.04 * black_beans + 3.8 * bananas + 9.58 * hamburgers <= 69)
m.addConstr(8.04 * black_beans + 7.77 * corn_cobs + 9.58 * hamburgers <= 104)
m.addConstr(2.96 * black_beans + 9.9 * hamburgers <= 48)
m.addConstr(2.96 * black_beans + 7.25 * bananas <= 163)
m.addConstr(7.25 * bananas + 1.68 * corn_cobs <= 88)
m.addConstr(2.96 * black_beans + 1.68 * corn_cobs <= 80)
m.addConstr(1.68 * corn_cobs + 9.9 * hamburgers <= 137)
m.addConstr(7.25 * bananas + 9.9 * hamburgers <= 62)
m.addConstr(8.47 * black_beans + 5.45 * corn_cobs + 0.76 * hamburgers <= 151)
m.addConstr(8.75 * black_beans + 8.76 * hamburgers <= 213)
m.addConstr(5.16 * bananas + 8.76 * hamburgers <= 101)
m.addConstr(5.16 * bananas + 2.4 * corn_cobs <= 180)
m.addConstr(8.75 * black_beans + 5.16 * bananas + 8.76 * hamburgers <= 75)


# 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)

```