## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin E' and 'milligrams of vitamin B6'. Let's denote 'milligrams of vitamin E' as $x_1$ and 'milligrams of vitamin B6' as $x_2$. The objective function to maximize is $9x_1 + 4x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- The energy stability index for $x_1$ is 7.36.
- The digestive support index for $x_1$ is 8.27.
- The energy stability index for $x_2$ is 4.97.
- The digestive support index for $x_2$ is 5.05.
- The total combined energy stability index from $x_1$ and $x_2$ must be 58 or more: $7.36x_1 + 4.97x_2 \geq 58$.
- The total combined digestive support index from $x_1$ and $x_2$ should be 25 at a minimum: $8.27x_1 + 5.05x_2 \geq 25$.
- $3x_1 - 4x_2 \geq 0$.
- The total combined energy stability index from $x_1$ and $x_2$ has to be as much or less than 91: $7.36x_1 + 4.97x_2 \leq 91$.
- The total combined digestive support index from $x_1$ and $x_2$ must be at most 47: $8.27x_1 + 5.05x_2 \leq 47$.

## 3: Correct and consolidate constraints for clarity
Upon review, some constraints seem to be repetitive or slightly misinterpreted. The correct and unique constraints are:
- $7.36x_1 + 4.97x_2 \geq 58$
- $8.27x_1 + 5.05x_2 \geq 25$
- $3x_1 - 4x_2 \geq 0$
- $7.36x_1 + 4.97x_2 \leq 91$
- $8.27x_1 + 5.05x_2 \leq 47$

## 4: Symbolic representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of vitamin B6')],
'objective_function': '9*x1 + 4*x2',
'constraints': [
    '7.36*x1 + 4.97*x2 >= 58',
    '8.27*x1 + 5.05*x2 >= 25',
    '3*x1 - 4*x2 >= 0',
    '7.36*x1 + 4.97*x2 <= 91',
    '8.27*x1 + 5.05*x2 <= 47'
]
}
```

## 5: Gurobi code
Now, let's implement this in Gurobi:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin E
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B6

    # Objective function: maximize 9*x1 + 4*x2
    model.setObjective(9*x1 + 4*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(7.36*x1 + 4.97*x2 >= 58)
    model.addConstr(8.27*x1 + 5.05*x2 >= 25)
    model.addConstr(3*x1 - 4*x2 >= 0)
    model.addConstr(7.36*x1 + 4.97*x2 <= 91)
    model.addConstr(8.27*x1 + 5.05*x2 <= 47)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin E: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```