```json
{
  "sym_variables": [
    ("x0", "milligrams of zinc"),
    ("x1", "milligrams of vitamin B2"),
    ("x2", "milligrams of vitamin B4"),
    ("x3", "milligrams of vitamin C")
  ],
  "objective_function": "7*x0 + 5*x1 + 4*x2 + 7*x3",
  "constraints": [
    "1*x0 + 25*x1 >= 47",
    "10*x2 + 16*x3 >= 44",
    "1*x0 + 16*x3 >= 32",
    "1*x0 + 25*x1 + 16*x3 >= 38",
    "1*x0 + 25*x1 + 10*x2 + 16*x3 >= 38",
    "15*x1 + 8*x2 >= 18",
    "21*x0 + 8*x2 >= 21",
    "8*x2 + 17*x3 >= 21",
    "21*x0 + 15*x1 >= 25",
    "21*x0 + 15*x1 + 8*x2 + 17*x3 >= 25",
    "9*x1 - 6*x2 >= 0",
    "10*x0 - 3*x1 >= 0",
    "1*x0 + 25*x1 <= 165",
    "25*x1 + 10*x2 <= 79",
    "1*x0 + 10*x2 <= 224",
    "15*x1 + 8*x2 + 17*x3 <= 113",
    "21*x0 + 15*x1 + 8*x2 <= 126",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    zinc = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="zinc")
    vitamin_b2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b2")
    vitamin_b4 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b4")
    vitamin_c = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_c")


    # Set objective function
    m.setObjective(7*zinc + 5*vitamin_b2 + 4*vitamin_b4 + 7*vitamin_c, gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(1*zinc + 25*vitamin_b2 >= 47)
    m.addConstr(10*vitamin_b4 + 16*vitamin_c >= 44)
    m.addConstr(1*zinc + 16*vitamin_c >= 32)
    m.addConstr(1*zinc + 25*vitamin_b2 + 16*vitamin_c >= 38)
    m.addConstr(1*zinc + 25*vitamin_b2 + 10*vitamin_b4 + 16*vitamin_c >= 38)
    m.addConstr(15*vitamin_b2 + 8*vitamin_b4 >= 18)
    m.addConstr(21*zinc + 8*vitamin_b4 >= 21)
    m.addConstr(8*vitamin_b4 + 17*vitamin_c >= 21)
    m.addConstr(21*zinc + 15*vitamin_b2 >= 25)
    m.addConstr(21*zinc + 15*vitamin_b2 + 8*vitamin_b4 + 17*vitamin_c >= 25)
    m.addConstr(9*vitamin_b2 - 6*vitamin_b4 >= 0)
    m.addConstr(10*zinc - 3*vitamin_b2 >= 0)
    m.addConstr(1*zinc + 25*vitamin_b2 <= 165)
    m.addConstr(25*vitamin_b2 + 10*vitamin_b4 <= 79)
    m.addConstr(1*zinc + 10*vitamin_b4 <= 224)
    m.addConstr(15*vitamin_b2 + 8*vitamin_b4 + 17*vitamin_c <= 113)
    m.addConstr(21*zinc + 15*vitamin_b2 + 8*vitamin_b4 <= 126)


    # Optimize model
    m.optimize()

    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('Optimization problem is infeasible.')
    else:
        print('Optimization ended with status %d' % m.status)


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```