```json
{
  "sym_variables": [
    ("x0", "cantaloupes"),
    ("x1", "apples"),
    ("x2", "kiwis"),
    ("x3", "chicken breasts")
  ],
  "objective_function": "6*x0 + 2*x1 + 2*x2 + 8*x3",
  "constraints": [
    "20*x0 + 16*x1 + 9*x2 + 19*x3 <= 220",
    "18*x0 + 18*x1 + 18*x2 + 23*x3 <= 87",
    "3*x0 + 9*x1 + 20*x2 + 5*x3 <= 290",
    "20*x0 + 16*x1 + 9*x2 >= 31",
    "3*x0 + 9*x1 >= 33",
    "16*x1 + 9*x2 <= 146",
    "20*x0 + 9*x2 <= 156",
    "9*x2 + 19*x3 <= 150",
    "20*x0 + 16*x1 + 9*x2 + 19*x3 <= 150",
    "18*x0 + 18*x1 <= 78",
    "18*x0 + 23*x3 <= 80",
    "18*x0 + 18*x1 + 18*x2 <= 65",
    "18*x1 + 18*x2 + 23*x3 <= 43",
    "18*x0 + 18*x2 + 23*x3 <= 34",
    "18*x0 + 18*x1 + 18*x2 + 23*x3 <= 34",
    "3*x0 + 9*x1 <= 224",
    "9*x1 + 5*x3 <= 274",
    "3*x0 + 20*x2 + 5*x3 <= 237",
    "3*x0 + 9*x1 + 20*x2 + 5*x3 <= 237",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
cantaloupes = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="cantaloupes")
apples = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="apples")
kiwis = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="kiwis")
chicken_breasts = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="chicken_breasts")

# Set objective function
m.setObjective(6*cantaloupes + 2*apples + 2*kiwis + 8*chicken_breasts, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(20*cantaloupes + 16*apples + 9*kiwis + 19*chicken_breasts <= 220, "calcium_upper_bound")
m.addConstr(18*cantaloupes + 18*apples + 18*kiwis + 23*chicken_breasts <= 87, "sourness_upper_bound")
m.addConstr(3*cantaloupes + 9*apples + 20*kiwis + 5*chicken_breasts <= 290, "cost_upper_bound")

m.addConstr(20*cantaloupes + 16*apples + 9*kiwis >= 31, "calcium_lower_bound")
m.addConstr(3*cantaloupes + 9*apples >= 33, "cantaloupes_apples_cost_lower_bound")

m.addConstr(16*apples + 9*kiwis <= 146, "apples_kiwis_calcium_upper_bound")
m.addConstr(20*cantaloupes + 9*kiwis <= 156, "cantaloupes_kiwis_calcium_upper_bound")
m.addConstr(9*kiwis + 19*chicken_breasts <= 150, "kiwis_chicken_calcium_upper_bound")
m.addConstr(20*cantaloupes + 16*apples + 9*kiwis + 19*chicken_breasts <= 150, "total_calcium_upper_bound")


m.addConstr(18*cantaloupes + 18*apples <= 78, "cantaloupes_apples_sourness_upper_bound")
m.addConstr(18*cantaloupes + 23*chicken_breasts <= 80, "cantaloupes_chicken_sourness_upper_bound")
m.addConstr(18*cantaloupes + 18*apples + 18*kiwis <= 65, "cantaloupes_apples_kiwis_sourness_upper_bound")
m.addConstr(18*apples + 18*kiwis + 23*chicken_breasts <= 43, "apples_kiwis_chicken_sourness_upper_bound")
m.addConstr(18*cantaloupes + 18*kiwis + 23*chicken_breasts <= 34, "cantaloupes_kiwis_chicken_sourness_upper_bound_2")
m.addConstr(18*cantaloupes + 18*apples + 18*kiwis + 23*chicken_breasts <= 34, "total_sourness_upper_bound")

m.addConstr(3*cantaloupes + 9*apples <= 224, "cantaloupes_apples_cost_upper_bound")
m.addConstr(9*apples + 5*chicken_breasts <= 274, "apples_chicken_cost_upper_bound")
m.addConstr(3*cantaloupes + 20*kiwis + 5*chicken_breasts <= 237, "cantaloupes_kiwis_chicken_cost_upper_bound")
m.addConstr(3*cantaloupes + 9*apples + 20*kiwis + 5*chicken_breasts <= 237, "total_cost_upper_bound")


# 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)

```