## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'grams of fat' and 'milligrams of vitamin B12', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $5.14x_1 + 5.3x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- $17x_1 \leq 52$ (cognitive performance index for grams of fat, but this seems to be an individual limit rather than a combined one, and similarly for others)
- $7x_1 \leq 176$ (cardiovascular support index for grams of fat)
- $17x_2 \leq 52$ (cognitive performance index for milligrams of vitamin B12)
- $17x_2 \leq 176$ (cardiovascular support index for milligrams of vitamin B12)
- $17x_1 + 17x_2 \geq 16$ (total combined cognitive performance index minimum)
- $7x_1 + 17x_2 \geq 48$ (total combined cardiovascular support index minimum)
- $-4x_1 + 10x_2 \geq 0$
- $17x_1 + 17x_2 \leq 38$ (total combined cognitive performance index maximum)
- $7x_1 + 17x_2 \leq 104$ (total combined cardiovascular support index maximum)

## 4: Correct and Simplify Constraints
Upon review, the individual limits for cognitive and cardiovascular support indices seem misinterpreted. The correct interpretation directly uses the provided attributes:
- Cognitive performance index for $x_1$ is 17, for $x_2$ is 17.
- Cardiovascular support index for $x_1$ is 7, for $x_2$ is 17.

## 5: Formulate the problem in Gurobi
We will use Gurobi to solve this optimization problem.

## 6: Write the Gurobi Code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define variables
x1 = model.addVar(name='grams_of_fat', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = model.addVar(name='milligrams_of_vitamin_B12', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Objective function
model.setObjective(5.14 * x1 + 5.3 * x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(17 <= 52, name='cognitive_performance_fat') # Not needed as direct limit
model.addConstr(7 * x1 <= 176, name='cardiovascular_support_fat')
model.addConstr(17 * x2 <= 52, name='cognitive_performance_vitamin')
model.addConstr(17 * x2 <= 176, name='cardiovascular_support_vitamin')
model.addConstr(17 * x1 + 17 * x2 >= 16, name='min_cognitive_performance')
model.addConstr(7 * x1 + 17 * x2 >= 48, name='min_cardiovascular_support')
model.addConstr(-4 * x1 + 10 * x2 >= 0, name='vitamin_fat_relation')
model.addConstr(17 * x1 + 17 * x2 <= 38, name='max_cognitive_performance')
model.addConstr(7 * x1 + 17 * x2 <= 104, name='max_cardiovascular_support')

# Optimize
model.optimize()

# Print solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Grams of fat: ', x1.varValue)
    print('Milligrams of vitamin B12: ', x2.varValue)
else:
    print('No optimal solution found')
```

## 7: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'grams of fat'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '5.14x1 + 5.3x2',
    'constraints': [
        '17x1 <= 52',
        '7x1 <= 176',
        '17x2 <= 52',
        '17x2 <= 176',
        '17x1 + 17x2 >= 16',
        '7x1 + 17x2 >= 48',
        '-4x1 + 10x2 >= 0',
        '17x1 + 17x2 <= 38',
        '7x1 + 17x2 <= 104'
    ]
}
```