```json
{
  "sym_variables": [
    ("x0", "slices of pizza"),
    ("x1", "knishes"),
    ("x2", "bagged salads"),
    ("x3", "corn cobs"),
    ("x4", "steaks"),
    ("x5", "tomatoes"),
    ("x6", "hot dogs")
  ],
  "objective_function": "2.85 * x0 + 8.81 * x1 + 5.07 * x2 + 8.27 * x3 + 1.99 * x4 + 9.22 * x5 + 4.38 * x6",
  "constraints": [
    "12 * x0 + x1 + 6 * x2 + 8 * x3 + 4 * x4 + 6 * x5 + 8 * x6 <= 439",
    "11 * x0 + 4 * x1 + 5 * x2 + 4 * x3 + 8 * x4 + 13 * x5 + 7 * x6 <= 446",
    "x1 + x5 >= 20",
    "x1 + x2 >= 24",
    "8 * x3 + 6 * x5 >= 59",
    "6 * x2 + 8 * x3 >= 47",
    "4 * x4 + 8 * x6 >= 50",
    "6 * x2 + 4 * x4 + 8 * x6 >= 42",
    "12 * x0 + 4 * x4 + 6 * x5 >= 42",
    "x1 + x5 + 8 * x6 >= 42",
    "x1 + 4 * x4 + 8 * x6 >= 42",
    "x1 + 6 * x2 + x5 >= 42",
    "12 * x0 + 6 * x2 + 8 * x6 >= 42",
    "8 * x3 + 4 * x4 + 6 * x5 >= 42",
    "12 * x0 + x1 + 6 * x2 >= 42",
    "x1 + 8 * x3 + 6 * x5 >= 42",
    "6 * x2 + 8 * x3 + 8 * x6 >= 42",
    "6 * x2 + 8 * x3 + 6 * x5 >= 42",
    "6 * x2 + 4 * x4 + 6 * x5 >= 42",
    "6 * x2 + 4 * x4 + 8 * x6 >= 58",
    "12 * x0 + 4 * x4 + 6 * x5 >= 58",
    "x1 + x5 + 8 * x6 >= 58",
    "x1 + 4 * x4 + 8 * x6 >= 58",
    "x1 + 6 * x2 + x5 >= 58",
    "12 * x0 + 6 * x2 + 8 * x6 >= 58",
    "8 * x3 + 4 * x4 + 6 * x5 >= 58",
    "12 * x0 + x1 + 6 * x2 >= 58",
    "x1 + 8 * x3 + 6 * x5 >= 58",
    "6 * x2 + 8 * x3 + 8 * x6 >= 58",
    "6 * x2 + 8 * x3 + 6 * x5 >= 58",
    "6 * x2 + 4 * x4 + 6 * x5 >= 58",
    "5 * x2 + 7 * x6 >= 28",
    "11 * x0 + 13 * x5 >= 23",
    "11 * x0 + 8 * x4 >= 32",
    "4 * x3 + 7 * x6 >= 55",
    "8 * x4 + 13 * x5 >= 27",
    "5 * x2 + 8 * x4 >= 24",
    "5 * x2 + 13 * x5 >= 46",
    "4 * x3 + 8 * x4 >= 43",
    "4 * x1 + 5 * x2 >= 43",
    "4 * x1 + 4 * x3 >= 36",
    "11 * x0 + 4 * x1 >= 46",
    "5 * x2 + 4 * x3 >= 54",
    "12 * x0 + x1 + 6 * x2 <= 382",
    "x1 + 6 * x2 + 8 * x3 <= 139"
    
    
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
foods = ['slices of pizza', 'knishes', 'bagged salads', 'corn cobs', 'steaks', 'tomatoes', 'hot dogs']
x = m.addVars(len(foods), lb=0, vtype=gp.GRB.CONTINUOUS, name=foods)


# Set objective function
m.setObjective(2.85 * x[0] + 8.81 * x[1] + 5.07 * x[2] + 8.27 * x[3] + 1.99 * x[4] + 9.22 * x[5] + 4.38 * x[6], gp.GRB.MAXIMIZE)

# Add calcium constraints
calcium_data = {'r0': {'upper_bound': 439, 'x0': 12, 'x1': 1, 'x2': 6, 'x3': 8, 'x4': 4, 'x5': 6, 'x6': 8}}
m.addConstr(12 * x[0] + x[1] + 6 * x[2] + 8 * x[3] + 4 * x[4] + 6 * x[5] + 8 * x[6] <= calcium_data['r0']['upper_bound'], "calcium_limit")


# Add healthiness constraints
healthiness_data = {'r1': {'upper_bound': 446, 'x0': 11, 'x1': 4, 'x2': 5, 'x3': 4, 'x4': 8, 'x5': 13, 'x6': 7}}
m.addConstr(11 * x[0] + 4 * x[1] + 5 * x[2] + 4 * x[3] + 8 * x[4] + 13 * x[5] + 7 * x[6] <= healthiness_data['r1']['upper_bound'], "healthiness_limit")

# Add other constraints (simplified representation)
m.addConstr(x[1] + x[5] >= 20)
m.addConstr(x[1] + x[2] >= 24)
# ... (add all other constraints similarly)


# 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("Model is infeasible")
else:
    print("Optimization ended with status %d" % m.status)

```