```json
{
  "sym_variables": [
    ("x0", "milligrams of magnesium"),
    ("x1", "milligrams of vitamin C")
  ],
  "objective_function": "7.49 * x0 + 3.26 * x1",
  "constraints": [
    "14 * x0 + 1 * x1 >= 23",
    "10 * x0 + -8 * x1 >= 0",
    "14 * x0 + 1 * x1 <= 67",
    "14 * x0 + 1 * x1 = 67"
  ]
}
```

The last two constraints are redundant, as they both specify an upper bound of 67 for the combined energy stability index.  We will keep the equality constraint and discard the inequality.  Also, note that x1 (milligrams of vitamin C) is constrained to be an integer.

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(lb=0, name="magnesium")  # milligrams of magnesium
    x1 = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="vitamin_c")  # milligrams of vitamin C

    # Set objective function
    m.setObjective(7.49 * x0 + 3.26 * x1, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(14 * x0 + 1 * x1 >= 23, "c0")
    m.addConstr(10 * x0 - 8 * x1 >= 0, "c1")
    m.addConstr(14 * x0 + 1 * x1 == 67, "c2")


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Objective Value:', m.objVal)
        print('Magnesium:', x0.x)
        print('Vitamin C:', x1.x)
    elif m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


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

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

```
