```json
{
  "sym_variables": [
    ("x0", "milligrams of magnesium"),
    ("x1", "grams of carbohydrates"),
    ("x2", "grams of fat"),
    ("x3", "milligrams of vitamin C")
  ],
  "objective_function": "4.55 * x0 + 1.85 * x1 + 5.52 * x2 + 5.81 * x3",
  "constraints": [
    "10 * x2 + 27 * x3 >= 25",
    "29 * x0 + 10 * x2 >= 33",
    "14 * x1 + 10 * x2 >= 21",
    "29 * x0 + 14 * x1 >= 19",
    "29 * x0 + 14 * x1 + 10 * x2 + 27 * x3 >= 19",
    "31 * x2 + 13 * x3 >= 22",
    "23 * x0 + 4 * x1 + 31 * x2 >= 30",
    "4 * x1 + 31 * x2 + 13 * x3 >= 30",
    "23 * x0 + 31 * x2 + 13 * x3 >= 30",
    "23 * x0 + 4 * x1 + 31 * x2 >= 28",
    "4 * x1 + 31 * x2 + 13 * x3 >= 28",
    "23 * x0 + 31 * x2 + 13 * x3 >= 28",
    "23 * x0 + 4 * x1 + 31 * x2 >= 27",
    "4 * x1 + 31 * x2 + 13 * x3 >= 27",
    "23 * x0 + 31 * x2 + 13 * x3 >= 27",
    "23 * x0 + 4 * x1 + 31 * x2 + 13 * x3 >= 27",
    "-6 * x0 + 10 * x2 >= 0",
    "29 * x0 + 10 * x2 <= 141",
    "29 * x0 + 27 * x3 <= 145",
    "10 * x2 + 27 * x3 <= 48",
    "29 * x0 + 14 * x1 + 27 * x3 <= 45",
    "14 * x1 + 10 * x2 + 27 * x3 <= 115",
    "23 * x0 + 13 * x3 <= 102",
    "23 * x0 + 31 * x2 <= 37",
    "29 * x0 + 14 * x1 <= 145",  // Added constraint for r0
    "23 * x0 + 4 * x1 + 31 * x2 + 13 * x3 <= 129" // Added constraint for r1
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
magnesium = m.addVar(lb=0, name="magnesium")
carbohydrates = m.addVar(lb=0, name="carbohydrates")
fat = m.addVar(lb=0, name="fat")
vitamin_c = m.addVar(lb=0, name="vitamin_c")

# Set objective function
m.setObjective(4.55 * magnesium + 1.85 * carbohydrates + 5.52 * fat + 5.81 * vitamin_c, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(10 * fat + 27 * vitamin_c >= 25)
m.addConstr(29 * magnesium + 10 * fat >= 33)
m.addConstr(14 * carbohydrates + 10 * fat >= 21)
m.addConstr(29 * magnesium + 14 * carbohydrates >= 19)
m.addConstr(29 * magnesium + 14 * carbohydrates + 10 * fat + 27 * vitamin_c >= 19)
m.addConstr(31 * fat + 13 * vitamin_c >= 22)
m.addConstr(23 * magnesium + 4 * carbohydrates + 31 * fat >= 30)
m.addConstr(4 * carbohydrates + 31 * fat + 13 * vitamin_c >= 30)
m.addConstr(23 * magnesium + 31 * fat + 13 * vitamin_c >= 30)
m.addConstr(23 * magnesium + 4 * carbohydrates + 31 * fat >= 28)
m.addConstr(4 * carbohydrates + 31 * fat + 13 * vitamin_c >= 28)
m.addConstr(23 * magnesium + 31 * fat + 13 * vitamin_c >= 28)
m.addConstr(23 * magnesium + 4 * carbohydrates + 31 * fat >= 27)
m.addConstr(4 * carbohydrates + 31 * fat + 13 * vitamin_c >= 27)
m.addConstr(23 * magnesium + 31 * fat + 13 * vitamin_c >= 27)
m.addConstr(23 * magnesium + 4 * carbohydrates + 31 * fat + 13 * vitamin_c >= 27)
m.addConstr(-6 * magnesium + 10 * fat >= 0)
m.addConstr(29 * magnesium + 10 * fat <= 141)
m.addConstr(29 * magnesium + 27 * vitamin_c <= 145)
m.addConstr(10 * fat + 27 * vitamin_c <= 48)
m.addConstr(29 * magnesium + 14 * carbohydrates + 27 * vitamin_c <= 45)
m.addConstr(14 * carbohydrates + 10 * fat + 27 * vitamin_c <= 115)
m.addConstr(23 * magnesium + 13 * vitamin_c <= 102)
m.addConstr(23 * magnesium + 31 * fat <= 37)

# Resource constraints
m.addConstr(29 * magnesium + 14 * carbohydrates + 10 * fat + 27 * vitamin_c <= 145)
m.addConstr(23 * magnesium + 4 * carbohydrates + 31 * fat + 13 * vitamin_c <= 129)


# 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)

```
