## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'grams of fiber' and 'milligrams of vitamin B4', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $8.29x_1 + 1.45x_2$. The constraints are:
- $12.24x_1 + 2.93x_2 \geq 10$
- $6.27x_1 + 6.76x_2 \geq 40$
- $-8x_1 + 2x_2 \geq 0$
- $12.24x_1 + 2.93x_2 \leq 17$
- $6.27x_1 + 6.76x_2 \leq 78$
- $x_1, x_2$ are continuous.

## 2: Convert the problem into a symbolic representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B4')],
'objective_function': '8.29*x1 + 1.45*x2',
'constraints': [
    '12.24*x1 + 2.93*x2 >= 10',
    '6.27*x1 + 6.76*x2 >= 40',
    '-8*x1 + 2*x2 >= 0',
    '12.24*x1 + 2.93*x2 <= 17',
    '6.27*x1 + 6.76*x2 <= 78'
]
}
```

## 3: Implement the problem using Gurobi
To solve this problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="grams_of_fiber", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = model.addVar(name="milligrams_of_vitamin_B4", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Define the objective function
model.setObjective(8.29 * x1 + 1.45 * x2, gurobi.GRB.MINIMIZE)

# Define the constraints
model.addConstr(12.24 * x1 + 2.93 * x2 >= 10, name="muscle_growth_index_min")
model.addConstr(6.27 * x1 + 6.76 * x2 >= 40, name="digestive_support_index_min")
model.addConstr(-8 * x1 + 2 * x2 >= 0, name="vitamin_B4_constraint")
model.addConstr(12.24 * x1 + 2.93 * x2 <= 17, name="muscle_growth_index_max")
model.addConstr(6.27 * x1 + 6.76 * x2 <= 78, name="digestive_support_index_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Grams of fiber: {x1.varValue}")
    print(f"Milligrams of vitamin B4: {x2.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```