```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B4"),
    ("x1", "milligrams of potassium"),
    ("x2", "milligrams of vitamin B1"),
    ("x3", "milligrams of vitamin B6")
  ],
  "objective_function": "2*x0 + 5*x1 + 8*x2 + 9*x3",
  "constraints": [
    "6.49*x0 + 7.23*x3 >= 49",
    "2.09*x0 + 9.55*x3 >= 34",
    "15.8*x1 + 13.81*x2 <= 259",
    "15.8*x1 + 13.81*x2 + 7.23*x3 <= 142",
    "6.49*x0 + 15.8*x1 + 13.81*x2 <= 156",
    "6.49*x0 + 15.8*x1 + 7.23*x3 <= 148",
    "6.49*x0 + 15.8*x1 + 13.81*x2 + 7.23*x3 <= 148",
    "4.16*x2 + 9.55*x3 <= 295",
    "0.7*x1 + 4.16*x2 <= 178",
    "0.7*x1 + 4.16*x2 + 9.55*x3 <= 113",
    "2.09*x0 + 0.7*x1 + 4.16*x2 <= 96",
    "2.09*x0 + 0.7*x1 + 4.16*x2 + 9.55*x3 <= 96",
    "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
    x = m.addVars(4, lb=0, vtype=gp.GRB.CONTINUOUS, name=["x0", "x1", "x2", "x3"])


    # Set objective function
    m.setObjective(2*x[0] + 5*x[1] + 8*x[2] + 9*x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(6.49*x[0] + 7.23*x[3] >= 49)
    m.addConstr(2.09*x[0] + 9.55*x[3] >= 34)
    m.addConstr(15.8*x[1] + 13.81*x[2] <= 259)
    m.addConstr(15.8*x[1] + 13.81*x[2] + 7.23*x[3] <= 142)
    m.addConstr(6.49*x[0] + 15.8*x[1] + 13.81*x[2] <= 156)
    m.addConstr(6.49*x[0] + 15.8*x[1] + 7.23*x[3] <= 148)
    m.addConstr(6.49*x[0] + 15.8*x[1] + 13.81*x[2] + 7.23*x[3] <= 148)
    m.addConstr(4.16*x[2] + 9.55*x[3] <= 295)
    m.addConstr(0.7*x[1] + 4.16*x[2] <= 178)
    m.addConstr(0.7*x[1] + 4.16*x[2] + 9.55*x[3] <= 113)
    m.addConstr(2.09*x[0] + 0.7*x[1] + 4.16*x[2] <= 96)
    m.addConstr(2.09*x[0] + 0.7*x[1] + 4.16*x[2] + 9.55*x[3] <= 96)


    # Optimize model
    m.optimize()

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


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

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