## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin E' and 'milligrams of vitamin B6'. Let's denote 'milligrams of vitamin E' as $x_1$ and 'milligrams of vitamin B6' as $x_2$. The objective function to minimize is $3x_1 + 2x_2$. The constraints are:
- $1.84x_1 + 1.53x_2 \geq 39$
- $-8x_1 + 9x_2 \geq 0$
- $1.84x_1 + 1.53x_2 \leq 44$
- $x_1, x_2 \geq 0$ (Implicitly, as there's no lower bound mentioned, but for optimization problems, non-negativity is often assumed unless stated otherwise)

## 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin E'
- $x_2$ for 'milligrams of vitamin B6'

The objective function is: $3x_1 + 2x_2$

The constraints are:
- $1.84x_1 + 1.53x_2 \geq 39$
- $-8x_1 + 9x_2 \geq 0$
- $1.84x_1 + 1.53x_2 \leq 44$

## 3: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '3*x1 + 2*x2',
    'constraints': [
        '1.84*x1 + 1.53*x2 >= 39',
        '-8*x1 + 9*x2 >= 0',
        '1.84*x1 + 1.53*x2 <= 44'
    ]
}
```

## 4: Implement the problem using Gurobi
To solve this problem using Gurobi, we'll first import the necessary library, then create a model, add variables and constraints according to the problem description.

```python
import gurobi

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

    # Add variables
    x1 = model.addVar(name="milligrams_of_vitamin_E", lb=0)  # Assuming non-negativity
    x2 = model.addVar(name="milligrams_of_vitamin_B6", lb=0)  # Assuming non-negativity

    # Objective function: Minimize 3*x1 + 2*x2
    model.setObjective(3*x1 + 2*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(1.84*x1 + 1.53*x2 >= 39, name="cardiovascular_support_index_min")
    model.addConstr(-8*x1 + 9*x2 >= 0, name="vitamin_constraint")
    model.addConstr(1.84*x1 + 1.53*x2 <= 44, name="cardiovascular_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin E: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```