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

Given:
- Variables: `x0` represents 'grams of fiber', and `x1` represents 'milligrams of vitamin B4'.
- Objective Function: Minimize `8.29*x0 + 1.45*x1`.
- Constraints:
  1. Muscle growth index from grams of fiber: `12.24*x0`
  2. Digestive support index from grams of fiber: `6.27*x0`
  3. Muscle growth index from milligrams of vitamin B4: `2.93*x1`
  4. Digestive support index from milligrams of vitamin B4: `6.76*x1`
  5. Total muscle growth index: `12.24*x0 + 2.93*x1 >= 10`
  6. Same as constraint 5, emphasizing the minimum requirement.
  7. Total digestive support index: `6.27*x0 + 6.76*x1 >= 40`
  8. Same as constraint 7, reiterating the minimum requirement.
  9. Linear combination of grams of fiber and milligrams of vitamin B4: `-8*x0 + 2*x1 >= 0`
  10. Maximum total muscle growth index: `12.24*x0 + 2.93*x1 <= 17`
  11. Maximum total digestive support index: `6.27*x0 + 6.76*x1 <= 78`

Symbolic Representation:
```json
{
  'sym_variables': [('x0', 'grams of fiber'), ('x1', 'milligrams of vitamin B4')],
  'objective_function': '8.29*x0 + 1.45*x1',
  'constraints': [
    '12.24*x0 + 2.93*x1 >= 10',
    '6.27*x0 + 6.76*x1 >= 40',
    '-8*x0 + 2*x1 >= 0',
    '12.24*x0 + 2.93*x1 <= 17',
    '6.27*x0 + 6.76*x1 <= 78'
  ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(lb=0, name="grams_of_fiber")  # Grams of fiber
x1 = m.addVar(lb=0, name="milligrams_of_vitamin_B4")  # Milligrams of vitamin B4

# Set the objective function
m.setObjective(8.29*x0 + 1.45*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(12.24*x0 + 2.93*x1 >= 10, name="min_muscle_growth_index")
m.addConstr(6.27*x0 + 6.76*x1 >= 40, name="min_digestive_support_index")
m.addConstr(-8*x0 + 2*x1 >= 0, name="linear_combination_constraint")
m.addConstr(12.24*x0 + 2.93*x1 <= 17, name="max_muscle_growth_index")
m.addConstr(6.27*x0 + 6.76*x1 <= 78, name="max_digestive_support_index")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of fiber: {x0.x}")
    print(f"Milligrams of vitamin B4: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```