```json
{
  "sym_variables": [
    ("x0", "bowls of pasta"),
    ("x1", "apples"),
    ("x2", "hot dogs")
  ],
  "objective_function": "2*x0 + 9*x1 + 5*x2",
  "constraints": [
    "7*x0 + 6*x1 + 9*x2 >= 13",
    "7*x1 + 7*x2 >= 17",
    "4*x0 + 7*x2 >= 15",
    "7*x1 + 2*x2 >= 22",
    "11*x0 + 2*x2 >= 18",
    "7*x0 + 9*x2 <= 25",
    "7*x0 + 6*x1 + 9*x2 <= 65",
    "4*x0 + 7*x2 <= 56",
    "7*x1 + 7*x2 <= 83",
    "4*x0 + 7*x1 + 7*x2 <= 93",
    "11*x0 + 2*x2 <= 84",
    "11*x0 + 7*x1 <= 100",
    "11*x0 + 7*x1 + 2*x2 <= 130",
    "10*x1 + 7*x2 <= 73",
    "10*x0 + 10*x1 + 7*x2 <= 91"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
pasta = m.addVar(lb=0, name="pasta")
apples = m.addVar(lb=0, name="apples")
hot_dogs = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="hot_dogs")


# Set objective function
m.setObjective(2 * pasta + 9 * apples + 5 * hot_dogs, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(7 * pasta + 6 * apples + 9 * hot_dogs >= 13, "iron_min")
m.addConstr(7 * apples + 7 * hot_dogs >= 17, "carbs_apples_hot_dogs_min")
m.addConstr(4 * pasta + 7 * hot_dogs >= 15, "carbs_pasta_hot_dogs_min")
m.addConstr(7 * apples + 2 * hot_dogs >= 22, "fiber_apples_hot_dogs_min")
m.addConstr(11 * pasta + 2 * hot_dogs >= 18, "fiber_pasta_hot_dogs_min")
m.addConstr(7 * pasta + 9 * hot_dogs <= 25, "iron_pasta_hot_dogs_max")

# Resource Constraints
m.addConstr(7 * pasta + 6 * apples + 9 * hot_dogs <= 65, "iron_max")
m.addConstr(4 * pasta + 7 * apples + 7 * hot_dogs <= 93, "carbs_max")
m.addConstr(11 * pasta + 7 * apples + 2 * hot_dogs <= 130, "fiber_max")
m.addConstr(10 * pasta + 10 * apples + 7 * hot_dogs <= 91, "cost_max")


m.addConstr(4 * pasta + 7 * hot_dogs <= 56, "carbs_pasta_hot_dogs_max")
m.addConstr(7 * apples + 7 * hot_dogs <= 83, "carbs_apples_hot_dogs_max")
m.addConstr(11 * pasta + 2 * hot_dogs <= 84, "fiber_pasta_hot_dogs_max")
m.addConstr(11 * pasta + 7 * apples <= 100, "fiber_pasta_apples_max")
m.addConstr(10 * apples + 7 * hot_dogs <= 73, "cost_apples_hot_dogs_max")



# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('pasta:', pasta.x)
    print('apples:', apples.x)
    print('hot_dogs:', hot_dogs.x)
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status:", m.status)

```
