## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B4', 'milligrams of vitamin C', and 'milligrams of vitamin B9'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the resources/attributes
The resources/attributes are given with their respective indices and values for each vitamin. We will use these to construct the constraints.

## Step 3: Formulate the objective function
The objective function to minimize is $3x_1 + 4x_2 + 5x_3$.

## 4: Formulate the constraints
The constraints based on the given problem are:
- Cognitive performance index: $2.57x_1 + 3.29x_2 \geq 28$
- Cognitive performance index: $3.29x_2 + 3.63x_3 \geq 54$
- Cognitive performance index: $2.57x_1 + 3.29x_2 + 3.63x_3 \geq 39$
- Digestive support index: $2.81x_1 + 0.14x_2 \geq 75$
- Digestive support index: $2.81x_1 + 0.97x_3 \geq 66$
- Digestive support index: $2.81x_1 + 0.14x_2 + 0.97x_3 \geq 66$
- Immune support index: $1.89x_1 + 0.17x_3 \geq 62$
- Immune support index: $1.89x_1 + 0.42x_2 + 0.17x_3 \geq 62$
- Energy stability index: $0.17x_2 + 3.03x_3 \geq 39$
- Energy stability index: $1.12x_1 + 3.03x_3 \geq 41$
- Energy stability index: $1.12x_1 + 0.17x_2 + 3.03x_3 \geq 41$
- Muscle growth index: $2.49x_1 + 3.07x_2 \geq 34$
- Muscle growth index: $2.49x_1 + 3.07x_2 + 2.49x_3 \geq 34$
- Cognitive performance index upper bound: $2.57x_1 + 3.29x_2 \leq 180$
- Cognitive performance index upper bound: $2.57x_1 + 3.63x_3 \leq 174$
- Immune support index upper bound: $1.89x_1 + 0.42x_2 \leq 89$
- Immune support index upper bound: $1.89x_1 + 0.17x_3 \leq 147$
- Immune support index upper bound: $0.42x_2 + 0.17x_3 \leq 63$
- Energy stability index upper bound: $1.12x_1 + 0.17x_2 \leq 97$

## 5: Convert the problem into Gurobi code
We will use the Gurobi Python API to model and solve this problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="milligrams of vitamin B4", lb=0)
x2 = m.addVar(name="milligrams of vitamin C", lb=0)
x3 = m.addVar(name="milligrams of vitamin B9", lb=0)

# Define the objective function
m.setObjective(3*x1 + 4*x2 + 5*x3, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(2.57*x1 + 3.29*x2 >= 28)
m.addConstr(3.29*x2 + 3.63*x3 >= 54)
m.addConstr(2.57*x1 + 3.29*x2 + 3.63*x3 >= 39)
m.addConstr(2.81*x1 + 0.14*x2 >= 75)
m.addConstr(2.81*x1 + 0.97*x3 >= 66)
m.addConstr(2.81*x1 + 0.14*x2 + 0.97*x3 >= 66)
m.addConstr(1.89*x1 + 0.17*x3 >= 62)
m.addConstr(1.89*x1 + 0.42*x2 + 0.17*x3 >= 62)
m.addConstr(0.17*x2 + 3.03*x3 >= 39)
m.addConstr(1.12*x1 + 3.03*x3 >= 41)
m.addConstr(1.12*x1 + 0.17*x2 + 3.03*x3 >= 41)
m.addConstr(2.49*x1 + 3.07*x2 >= 34)
m.addConstr(2.49*x1 + 3.07*x2 + 2.49*x3 >= 34)
m.addConstr(2.57*x1 + 3.29*x2 <= 180)
m.addConstr(2.57*x1 + 3.63*x3 <= 174)
m.addConstr(1.89*x1 + 0.42*x2 <= 89)
m.addConstr(1.89*x1 + 0.17*x3 <= 147)
m.addConstr(0.42*x2 + 0.17*x3 <= 63)
m.addConstr(1.12*x1 + 0.17*x2 <= 97)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Milligrams of vitamin B4: ", x1.varValue)
    print("Milligrams of vitamin C: ", x2.varValue)
    print("Milligrams of vitamin B9: ", x3.varValue)
else:
    print("The problem is infeasible")
```

## 6: Symbolic representation of the problem
The symbolic representation is as follows:
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B4'), 
        ('x2', 'milligrams of vitamin C'), 
        ('x3', 'milligrams of vitamin B9')
    ], 
    'objective_function': '3*x1 + 4*x2 + 5*x3', 
    'constraints': [
        '2.57*x1 + 3.29*x2 >= 28',
        '3.29*x2 + 3.63*x3 >= 54',
        '2.57*x1 + 3.29*x2 + 3.63*x3 >= 39',
        '2.81*x1 + 0.14*x2 >= 75',
        '2.81*x1 + 0.97*x3 >= 66',
        '2.81*x1 + 0.14*x2 + 0.97*x3 >= 66',
        '1.89*x1 + 0.17*x3 >= 62',
        '1.89*x1 + 0.42*x2 + 0.17*x3 >= 62',
        '0.17*x2 + 3.03*x3 >= 39',
        '1.12*x1 + 3.03*x3 >= 41',
        '1.12*x1 + 0.17*x2 + 3.03*x3 >= 41',
        '2.49*x1 + 3.07*x2 >= 34',
        '2.49*x1 + 3.07*x2 + 2.49*x3 >= 34',
        '2.57*x1 + 3.29*x2 <= 180',
        '2.57*x1 + 3.63*x3 <= 174',
        '1.89*x1 + 0.42*x2 <= 89',
        '1.89*x1 + 0.17*x3 <= 147',
        '0.42*x2 + 0.17*x3 <= 63',
        '1.12*x1 + 0.17*x2 <= 97'
    ]
}
```