To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and constraints using mathematical notation.

Let's denote:
- $x_1$ as the milligrams of vitamin E,
- $x_2$ as the milligrams of vitamin B6.

The objective function is given as: Maximize $9x_1 + 4x_2$.

Constraints are defined based on the problem description:
1. The total combined energy stability index from milligrams of vitamin E plus milligrams of vitamin B6 must be 58 or more: $7.36x_1 + 4.97x_2 \geq 58$.
2. The total combined digestive support index from milligrams of vitamin E plus milligrams of vitamin B6 should be 25 at a minimum: $8.27x_1 + 5.05x_2 \geq 25$.
3. $3x_1 - 4x_2 \geq 0$.
4. The total combined energy stability index from milligrams of vitamin E plus milligrams of vitamin B6 has to be as much or less than 91: $7.36x_1 + 4.97x_2 \leq 116$ is not a constraint given the upper bounds provided in the resources; instead, we use $7.36x_1 + 4.97x_2 \leq 91$ as per the problem's direct constraints.
5. The total combined digestive support index from milligrams of vitamin E plus milligrams of vitamin B6 must be at most 47: $8.27x_1 + 5.05x_2 \leq 47$.

The symbolic representation is thus:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': 'Maximize 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'
    ]
}
```

To implement this problem using Gurobi, we will write the following Python code:

```python
from gurobipy import *

# Create a new model
m = Model("Vitamin_Optimization")

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_E")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B6")

# Set the objective function
m.setObjective(9*x1 + 4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7.36*x1 + 4.97*x2 >= 58, "energy_stability_index_min")
m.addConstr(8.27*x1 + 5.05*x2 >= 25, "digestive_support_index_min")
m.addConstr(3*x1 - 4*x2 >= 0, "vitamin_ratio_constraint")
m.addConstr(7.36*x1 + 4.97*x2 <= 91, "energy_stability_index_max")
m.addConstr(8.27*x1 + 5.05*x2 <= 47, "digestive_support_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin E: {x1.x}")
    print(f"Milligrams of Vitamin B6: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```