## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B9' and 'milligrams of vitamin B6', which can be represented symbolically as $x_1$ and $x_2$ respectively. The objective function to minimize is $2x_1 + 3x_2$. The constraints are:
- $10x_1 \leq 85$
- $1x_1 \leq 35$
- $8x_2 \leq 85$
- $5x_2 \leq 35$
- $10x_1 + 8x_2 \geq 35$
- $10x_1 + 8x_2 \geq 35$ (redundant with the previous one)
- $1x_1 + 5x_2 \geq 14$
- $1x_1 + 5x_2 \geq 14$ (redundant with the previous one)
- $-4x_1 + 10x_2 \geq 0$
- $10x_1 + 8x_2 \leq 54$
- $1x_1 + 5x_2 \leq 33$

## 2: Simplify and correct the constraints
Upon review, we see that some constraints are redundant. The correct set of unique constraints is:
- $x_1 \leq 8.5$ (from $10x_1 \leq 85$)
- $x_1 \leq 35$
- $x_2 \leq 10.625$ (from $8x_2 \leq 85$)
- $x_2 \leq 7$ (from $5x_2 \leq 35$)
- $10x_1 + 8x_2 \geq 35$
- $x_1 + 5x_2 \geq 14$
- $-4x_1 + 10x_2 \geq 0$
- $10x_1 + 8x_2 \leq 54$
- $x_1 + 5x_2 \leq 33$

## 3: Convert the problem into a Gurobi-compatible format
We will use Gurobi's Python API to model and solve this problem.

## 4: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_B9", lb=0, ub=None)
    x2 = model.addVar(name="milligrams_of_vitamin_B6", lb=0, ub=None)

    # Define the objective function
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(x1 <= 8.5)  # from r0 of vitamin B9
    model.addConstr(x1 <= 35)   # from r1 of vitamin B9
    model.addConstr(x2 <= 10.625)  # from r0 of vitamin B6
    model.addConstr(x2 <= 7)      # from r1 of vitamin B6
    model.addConstr(10*x1 + 8*x2 >= 35)  # total energy stability index
    model.addConstr(x1 + 5*x2 >= 14)    # total cardiovascular support index
    model.addConstr(-4*x1 + 10*x2 >= 0) # specific linear constraint
    model.addConstr(10*x1 + 8*x2 <= 54) # upper bound on total energy stability index
    model.addConstr(x1 + 5*x2 <= 33)    # upper bound on total cardiovascular support index

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B9: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 5: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B9'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        'x1 <= 8.5',
        'x1 <= 35',
        'x2 <= 10.625',
        'x2 <= 7',
        '10*x1 + 8*x2 >= 35',
        'x1 + 5*x2 >= 14',
        '-4*x1 + 10*x2 >= 0',
        '10*x1 + 8*x2 <= 54',
        'x1 + 5*x2 <= 33'
    ]
}
```