```json
{
  "sym_variables": [
    ("x0", "grams of carbohydrates"),
    ("x1", "milligrams of vitamin B5"),
    ("x2", "milligrams of vitamin B4")
  ],
  "objective_function": "5.42*x0**2 + 3.59*x0*x1 + 4.15*x0*x2 + 5.43*x1*x2 + 7.08*x2**2 + 6.25*x0 + 7.4*x1 + 6.3*x2",
  "constraints": [
    "20*x0 + 8*x2 >= 61",
    "16*x1 + 8*x2 >= 87",
    "20*x0**2 + 16*x1**2 >= 91",
    "20*x0 + 16*x1 + 8*x2 >= 91",
    "13*x1**2 + 5*x2**2 >= 26",
    "20*x0 + 13*x1 + 5*x2 >= 26",
    "11*x1 + 10*x2 >= 18",
    "1*x0 + 11*x1 + 10*x2 >= 18",
    "-6*x0 + 8*x1 >= 0",
    "20*x0 + 5*x2 <= 95",
    "20*x0 + 13*x1 <= 98",
    "13*x1 + 5*x2 <= 91",
    "20*x0 <= 281",
    "13*x1 <= 98",
    "5*x2 <= 92"

  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x0") # grams of carbohydrates
    x1 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x1") # milligrams of vitamin B5
    x2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x2") # milligrams of vitamin B4


    # Set objective function
    m.setObjective(5.42*x0**2 + 3.59*x0*x1 + 4.15*x0*x2 + 5.43*x1*x2 + 7.08*x2**2 + 6.25*x0 + 7.4*x1 + 6.3*x2, gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(20*x0 + 8*x2 >= 61, "c0")
    m.addConstr(16*x1 + 8*x2 >= 87, "c1")
    m.addConstr(20*x0**2 + 16*x1**2 >= 91, "c2")
    m.addConstr(20*x0 + 16*x1 + 8*x2 >= 91, "c3")
    m.addConstr(13*x1**2 + 5*x2**2 >= 26, "c4")
    m.addConstr(20*x0 + 13*x1 + 5*x2 >= 26, "c5")
    m.addConstr(11*x1 + 10*x2 >= 18, "c6")
    m.addConstr(1*x0 + 11*x1 + 10*x2 >= 18, "c7")
    m.addConstr(-6*x0 + 8*x1 >= 0, "c8")
    m.addConstr(20*x0 + 5*x2 <= 95, "c9")
    m.addConstr(20*x0 + 13*x1 <= 98, "c10")
    m.addConstr(13*x1 + 5*x2 <= 91, "c11")

    # Resource Constraints
    m.addConstr(20*x0 <= 281, "r0")
    m.addConstr(13*x1 <= 98, "r1")
    m.addConstr(5*x2 <= 92, "r2")


    # 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')
```