To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Given variables:
- `x1`: milligrams of vitamin E
- `x2`: milligrams of vitamin B6

Objective Function: Minimize `3*x1 + 2*x2`

Constraints:
1. The total combined cardiovascular support index from milligrams of vitamin E plus milligrams of vitamin B6 must be at least 39.
   - `1.84*x1 + 1.53*x2 >= 39`
2. The constraint that the total combined cardiovascular support index should be at least 39 is repeated, so we consider it as part of the first constraint.
3. `-8*x1 + 9*x2 >= 0`
4. The total combined cardiovascular support index from milligrams of vitamin E plus milligrams of vitamin B6 should be less than or equal to 44.
   - `1.84*x1 + 1.53*x2 <= 44`

Symbolic Representation:
```json
{
  'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of vitamin B6')],
  'objective_function': 'Minimize 3*x1 + 2*x2',
  'constraints': [
    '1.84*x1 + 1.53*x2 >= 39',
    '-8*x1 + 9*x2 >= 0',
    '1.84*x1 + 1.53*x2 <= 44'
  ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
model = Model("Vitamin_Optimization")

# Add variables to the model
x1 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_E")
x2 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B6")

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

# Add constraints
model.addConstr(1.84*x1 + 1.53*x2 >= 39, "cardiovascular_support_min")
model.addConstr(-8*x1 + 9*x2 >= 0, "vitamin_balance")
model.addConstr(1.84*x1 + 1.53*x2 <= 44, "cardiovascular_support_max")

# Optimize model
model.optimize()

# Print solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin E: {x1.x}")
    print(f"Milligrams of Vitamin B6: {x2.x}")
    print(f"Objective Function Value: {model.ObjVal}")
else:
    print("No optimal solution found")
```