```json
{
  "sym_variables": [
    ("x0", "peanutbutter sandwiches"),
    ("x1", "chicken thighs"),
    ("x2", "sashimi"),
    ("x3", "apple pies")
  ],
  "objective_function": "6.81*x0 + 5.9*x1 + 9.09*x2 + 4.3*x3",
  "constraints": [
    "6*x0 + 5*x1 >= 21",
    "5*x1 + 4*x2 >= 19",
    "6*x0 + 5*x1 + 4*x2 >= 24",
    "6*x0 + 4*x2 + 10*x3 >= 24",
    "5*x1 + 4*x2 + 10*x3 >= 24",
    "6*x0 + 5*x1 + 4*x2 >= 12",
    "6*x0 + 4*x2 + 10*x3 >= 12",
    "5*x1 + 4*x2 + 10*x3 >= 12",
    "6*x0 + 5*x1 + 4*x2 >= 13",
    "6*x0 + 4*x2 + 10*x3 >= 13",
    "5*x1 + 4*x2 + 10*x3 >= 13",
    "6*x0 + 5*x1 + 4*x2 + 10*x3 >= 13",
    "9*x0 + 1*x3 >= 16",
    "2*x2 + 1*x3 >= 13",
    "9*x0 + 4*x1 >= 8",
    "4*x1 + 2*x2 >= 11",
    "4*x1 + 1*x3 >= 9",
    "9*x0 + 4*x1 + 2*x2 + 1*x3 >= 9",
    "2*x1 - 8*x3 >= 0",
    "9*x2 - 8*x3 >= 0",
    "5*x1 + 4*x2 <= 91",
    "5*x1 + 10*x3 <= 57",
    "4*x1 + 1*x3 <= 16",
    "9*x0 + 2*x2 <= 34",
    "6*x0 + 5*x1 + 4*x2 + 10*x3 <= 97",  // Iron upper bound
    "9*x0 + 4*x1 + 2*x2 + 1*x3 <= 66"   // Tastiness upper bound
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
x = m.addVars(4, lb=0, vtype=gp.GRB.CONTINUOUS, name=["peanutbutter sandwiches", "chicken thighs", "sashimi", "apple pies"])


# Set objective function
m.setObjective(6.81 * x[0] + 5.9 * x[1] + 9.09 * x[2] + 4.3 * x[3], gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(6 * x[0] + 5 * x[1] >= 21)
m.addConstr(5 * x[1] + 4 * x[2] >= 19)
m.addConstr(6 * x[0] + 5 * x[1] + 4 * x[2] >= 24)
m.addConstr(6 * x[0] + 4 * x[2] + 10 * x[3] >= 24)
m.addConstr(5 * x[1] + 4 * x[2] + 10 * x[3] >= 24)
m.addConstr(6 * x[0] + 5 * x[1] + 4 * x[2] >= 12)
m.addConstr(6 * x[0] + 4 * x[2] + 10 * x[3] >= 12)
m.addConstr(5 * x[1] + 4 * x[2] + 10 * x[3] >= 12)
m.addConstr(6 * x[0] + 5 * x[1] + 4 * x[2] >= 13)
m.addConstr(6 * x[0] + 4 * x[2] + 10 * x[3] >= 13)
m.addConstr(5 * x[1] + 4 * x[2] + 10 * x[3] >= 13)
m.addConstr(6 * x[0] + 5 * x[1] + 4 * x[2] + 10 * x[3] >= 13)
m.addConstr(9 * x[0] + x[3] >= 16)
m.addConstr(2 * x[2] + x[3] >= 13)
m.addConstr(9 * x[0] + 4 * x[1] >= 8)
m.addConstr(4 * x[1] + 2 * x[2] >= 11)
m.addConstr(4 * x[1] + x[3] >= 9)
m.addConstr(9 * x[0] + 4 * x[1] + 2 * x[2] + x[3] >= 9)
m.addConstr(2 * x[1] - 8 * x[3] >= 0)
m.addConstr(9 * x[2] - 8 * x[3] >= 0)
m.addConstr(5 * x[1] + 4 * x[2] <= 91)
m.addConstr(5 * x[1] + 10 * x[3] <= 57)
m.addConstr(4 * x[1] + x[3] <= 16)
m.addConstr(9 * x[0] + 2 * x[2] <= 34)

# Resource constraints (iron and tastiness)
m.addConstr(6 * x[0] + 5 * x[1] + 4 * x[2] + 10 * x[3] <= 97)
m.addConstr(9 * x[0] + 4 * x[1] + 2 * x[2] + x[3] <= 66)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    for v in m.getVars():
        print(f'{v.varName}: {v.x}')
    print(f'Obj: {m.objVal}')
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```