## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B6' and 'milligrams of vitamin A'. Let's denote 'milligrams of vitamin B6' as $x_1$ and 'milligrams of vitamin A' as $x_2$. The objective function to maximize is $5.62x_1 + 8.21x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $23x_1 \leq 131$
- $26x_1 \leq 78$
- $12x_1 \leq 86$
- $11x_1 \leq 48$
- $17x_1 \leq 75$
- $10x_2 \leq 131$
- $23x_2 \leq 78$
- $21x_2 \leq 86$
- $18x_2 \leq 48$
- $16x_2 \leq 75$
- $23x_1 + 10x_2 \geq 40$
- $26x_1 + 23x_2 \geq 35$
- $12x_1 + 21x_2 \geq 36$
- $11x_1 + 18x_2 \geq 16$
- $17x_1 + 16x_2 \geq 28$
- $-x_1 + 10x_2 \geq 0$
- $23x_1 + 10x_2 \leq 68$
- $26x_1 + 23x_2 \leq 76$
- $12x_1 + 21x_2 \leq 60$
- $11x_1 + 18x_2 \leq 26$
- $17x_1 + 16x_2 \leq 49$

## 3: Correct and simplify constraints based on given resources/attributes
Upon reviewing, notice that some constraints seem to be directly from the resources/attributes given but are not properly scaled. The correct approach is to directly use the given upper bounds and convert the problem description into a standard optimization problem.

## 4: Formulate the symbolic representation
Given:
- $x_1$ represents 'milligrams of vitamin B6'
- $x_2$ represents 'milligrams of vitamin A'

The objective function is: $5.62x_1 + 8.21x_2$

Constraints:
1. $x_1 \geq 0$, $x_2$ is non-negative but not necessarily an integer for $x_1$, and $x_2$ must be an integer.

However, given $x_2$ needs to be a non-fractional (implying integer) amount:
- $x_2 \in \mathbb{Z}$

## 5: Convert problem into Gurobi code
```python
import gurobi

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

# Define variables
x1 = model.addVar(lb=0, name="milligrams_of_vitamin_B6")
x2 = model.addVar(lb=0, type=gurobi.GRB.INTEGER, name="milligrams_of_vitamin_A")

# Objective function
model.setObjective(5.62 * x1 + 8.21 * x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(23 * x1 <= 131, name="energy_stability_index_B6")
model.addConstr(26 * x1 <= 78, name="immune_support_index_B6")
model.addConstr(12 * x1 <= 86, name="muscle_growth_index_B6")
model.addConstr(11 * x1 <= 48, name="cognitive_performance_index_B6")
model.addConstr(17 * x1 <= 75, name="cardiovascular_support_index_B6")

model.addConstr(10 * x2 <= 131, name="energy_stability_index_A")
model.addConstr(23 * x2 <= 78, name="immune_support_index_A")
model.addConstr(21 * x2 <= 86, name="muscle_growth_index_A")
model.addConstr(18 * x2 <= 48, name="cognitive_performance_index_A")
model.addConstr(16 * x2 <= 75, name="cardiovascular_support_index_A")

model.addConstr(23 * x1 + 10 * x2 >= 40, name="combined_energy_stability_index")
model.addConstr(26 * x1 + 23 * x2 >= 35, name="combined_immune_support_index")
model.addConstr(12 * x1 + 21 * x2 >= 36, name="combined_muscle_growth_index")
model.addConstr(11 * x1 + 18 * x2 >= 16, name="combined_cognitive_performance_index")
model.addConstr(17 * x1 + 16 * x2 >= 28, name="combined_cardiovascular_support_index")

model.addConstr(-x1 + 10 * x2 >= 0, name="vitamin_A_B6_relation")

model.addConstr(23 * x1 + 10 * x2 <= 68, name="combined_energy_stability_index_upper")
model.addConstr(26 * x1 + 23 * x2 <= 76, name="combined_immune_support_index_upper")
model.addConstr(12 * x1 + 21 * x2 <= 60, name="combined_muscle_growth_index_upper")
model.addConstr(11 * x1 + 18 * x2 <= 26, name="combined_cognitive_performance_index_upper")
model.addConstr(17 * x1 + 16 * x2 <= 49, name="combined_cardiovascular_support_index_upper")

# Optimize
model.optimize()

# Print solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B6: {x1.varValue}")
    print(f"Milligrams of vitamin A: {x2.varValue}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of vitamin A')],
    'objective_function': '5.62*x1 + 8.21*x2',
    'constraints': [
        '23*x1 <= 131',
        '26*x1 <= 78',
        '12*x1 <= 86',
        '11*x1 <= 48',
        '17*x1 <= 75',
        '10*x2 <= 131',
        '23*x2 <= 78',
        '21*x2 <= 86',
        '18*x2 <= 48',
        '16*x2 <= 75',
        '23*x1 + 10*x2 >= 40',
        '26*x1 + 23*x2 >= 35',
        '12*x1 + 21*x2 >= 36',
        '11*x1 + 18*x2 >= 16',
        '17*x1 + 16*x2 >= 28',
        '-x1 + 10*x2 >= 0',
        '23*x1 + 10*x2 <= 68',
        '26*x1 + 23*x2 <= 76',
        '12*x1 + 21*x2 <= 60',
        '11*x1 + 18*x2 <= 26',
        '17*x1 + 16*x2 <= 49'
    ]
}
```