```json
{
  "sym_variables": [
    ("x0", "grams of protein"),
    ("x1", "milligrams of potassium"),
    ("x2", "milligrams of vitamin B4"),
    ("x3", "milligrams of vitamin B3")
  ],
  "objective_function": "8.14*x0 + 4.73*x1 + 7.96*x2 + 3.19*x3",
  "constraints": [
    "6*x0 + 28*x1 + 1*x2 + 22*x3 >= 127",
    "6*x0 + 28*x1 >= 104",
    "28*x1 + 22*x3 >= 147",
    "1*x2 + 22*x3 >= 167",
    "6*x0 + 28*x1 + 22*x3 >= 111",
    "6*x0 + 28*x1 + 1*x2 + 22*x3 >= 111",
    "5*x0 + 9*x2 >= 91",
    "5*x0 + 8*x3 >= 71",
    "4*x1 + 9*x2 >= 114",
    "5*x0 + 4*x1 + 9*x2 + 8*x3 >= 114",
    "28*x0 + 12*x3 >= 121",
    "28*x0 + 26*x2 >= 68",
    "26*x2 + 12*x3 >= 108",
    "28*x0 + 26*x1 >= 120",
    "26*x1 + 12*x3 >= 108",
    "28*x0 + 26*x1 + 12*x3 >= 108",
    "28*x0 + 26*x2 + 12*x3 >= 108",
    "28*x0 + 26*x1 + 12*x3 >= 146",
    "28*x0 + 26*x2 + 12*x3 >= 146",
    "28*x0 + 26*x1 + 26*x2 + 12*x3 >= 146",
    "1*x2 - 5*x3 >= 0",
    "1*x2 + 22*x3 <= 553",
    "28*x1 + 1*x2 <= 554",
    "6*x0 + 22*x3 <= 231",
    "6*x0 + 28*x1 <= 178",
    "6*x0 + 28*x1 + 22*x3 <= 508",
    "6*x0 + 1*x2 + 22*x3 <= 304",
    "5*x0 + 4*x1 <= 411",
    "5*x0 + 8*x3 <= 539",
    "4*x1 + 9*x2 <= 357",
    "4*x1 + 8*x3 <= 445",
    "5*x0 + 9*x2 <= 266",
    "5*x0 + 4*x1 + 8*x3 <= 459",
    "5*x0 + 9*x2 + 8*x3 <= 322",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
protein = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="protein")
potassium = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="potassium")
vitamin_b4 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b4")
vitamin_b3 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b3")

# Set objective function
m.setObjective(8.14 * protein + 4.73 * potassium + 7.96 * vitamin_b4 + 3.19 * vitamin_b3, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(6 * protein + 28 * potassium + 1 * vitamin_b4 + 22 * vitamin_b3 >= 127)
m.addConstr(6 * protein + 28 * potassium >= 104)
m.addConstr(28 * potassium + 22 * vitamin_b3 >= 147)
m.addConstr(1 * vitamin_b4 + 22 * vitamin_b3 >= 167)
m.addConstr(6 * protein + 28 * potassium + 22 * vitamin_b3 >= 111)
m.addConstr(6 * protein + 28 * potassium + 1 * vitamin_b4 + 22 * vitamin_b3 >= 111)
m.addConstr(5 * protein + 9 * vitamin_b4 >= 91)
m.addConstr(5 * protein + 8 * vitamin_b3 >= 71)
m.addConstr(4 * potassium + 9 * vitamin_b4 >= 114)
m.addConstr(5 * protein + 4 * potassium + 9 * vitamin_b4 + 8 * vitamin_b3 >= 114)
m.addConstr(28 * protein + 12 * vitamin_b3 >= 121)
m.addConstr(28 * protein + 26 * vitamin_b4 >= 68)
m.addConstr(26 * vitamin_b4 + 12 * vitamin_b3 >= 108)
m.addConstr(28 * protein + 26 * potassium >= 120)
m.addConstr(26 * potassium + 12 * vitamin_b3 >= 108)
m.addConstr(28 * protein + 26 * potassium + 12 * vitamin_b3 >= 108)
m.addConstr(28 * protein + 26 * vitamin_b4 + 12 * vitamin_b3 >= 108)
m.addConstr(28 * protein + 26 * potassium + 12 * vitamin_b3 >= 146)
m.addConstr(28 * protein + 26 * vitamin_b4 + 12 * vitamin_b3 >= 146)
m.addConstr(28 * protein + 26 * potassium + 26 * vitamin_b4 + 12 * vitamin_b3 >= 146)
m.addConstr(1 * vitamin_b4 - 5 * vitamin_b3 >= 0)
m.addConstr(1 * vitamin_b4 + 22 * vitamin_b3 <= 553)
m.addConstr(28 * potassium + 1 * vitamin_b4 <= 554)
m.addConstr(6 * protein + 22 * vitamin_b3 <= 231)
m.addConstr(6 * protein + 28 * potassium <= 178)
m.addConstr(6 * protein + 28 * potassium + 22 * vitamin_b3 <= 508)
m.addConstr(6 * protein + 1 * vitamin_b4 + 22 * vitamin_b3 <= 304)
m.addConstr(5 * protein + 4 * potassium <= 411)
m.addConstr(5 * protein + 8 * vitamin_b3 <= 539)
m.addConstr(4 * potassium + 9 * vitamin_b4 <= 357)
m.addConstr(4 * potassium + 8 * vitamin_b3 <= 445)
m.addConstr(5 * protein + 9 * vitamin_b4 <= 266)
m.addConstr(5 * protein + 4 * potassium + 8 * vitamin_b3 <= 459)
m.addConstr(5 * protein + 9 * vitamin_b4 + 8 * vitamin_b3 <= 322)


# 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)

```