## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of iron' and 'milligrams of vitamin B9'. Let's denote 'milligrams of iron' as $x_1$ and 'milligrams of vitamin B9' as $x_2$. The objective function to minimize is $6.68x_1 + 7.55x_2$.

## 2: List the constraints in symbolic notation
The constraints given are:
1. The cardiovascular support index of milligrams of iron is 0.4, which translates to $0.4x_1$.
2. The cardiovascular support index for milligrams of vitamin B9 is 0.14, which translates to $0.14x_2$.
3. The total combined cardiovascular support index from milligrams of iron plus milligrams of vitamin B9 must be 47 at minimum: $0.4x_1 + 0.14x_2 \geq 47$.
4. The total combined cardiovascular support index from milligrams of iron, and milligrams of vitamin B9 should be no less than 47: This is the same as the previous constraint, $0.4x_1 + 0.14x_2 \geq 47$.
5. -3 times the number of milligrams of iron, plus 4 times the number of milligrams of vitamin B9 has to be greater than or equal to zero: $-3x_1 + 4x_2 \geq 0$.
6. The total combined cardiovascular support index from milligrams of iron plus milligrams of vitamin B9 must be at maximum 104: $0.4x_1 + 0.14x_2 \leq 104$.

## 3: Define the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of iron'), ('x2', 'milligrams of vitamin B9')],
'objective_function': '6.68*x1 + 7.55*x2',
'constraints': [
    '0.4*x1 = 0.4',
    '0.14*x2 = 0.14',
    '0.4*x1 + 0.14*x2 >= 47',
    '-3*x1 + 4*x2 >= 0',
    '0.4*x1 + 0.14*x2 <= 104'
]
}
```

## 4: Convert the problem into Gurobi code
To solve this problem using Gurobi, we will write the following Python code:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_iron", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="milligrams_of_vitamin_B9", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Set the objective function
    model.setObjective(6.68 * x1 + 7.55 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(0.4 * x1 == 0.4, name="cardiovascular_support_index_iron")
    model.addConstr(0.14 * x2 == 0.14, name="cardiovascular_support_index_vitamin_B9")
    model.addConstr(0.4 * x1 + 0.14 * x2 >= 47, name="combined_cardiovascular_support_index_min")
    model.addConstr(-3 * x1 + 4 * x2 >= 0, name="iron_vitamin_B9_constraint")
    model.addConstr(0.4 * x1 + 0.14 * x2 <= 104, name="combined_cardiovascular_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the status of the model
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of iron: {x1.varValue}")
        print(f"Milligrams of vitamin B9: {x2.varValue}")
        print(f"Objective function value: {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()
```