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 in terms of these variables.

Let's denote:
- $x_1$ as the milligrams of iron,
- $x_2$ as the milligrams of vitamin B9.

The objective function to be minimized is: $6.68x_1 + 7.55x_2$

The constraints are:
1. $0.4x_1 + 0.14x_2 \geq 47$
2. $-3x_1 + 4x_2 \geq 0$
3. $0.4x_1 + 0.14x_2 \leq 104$

Note that the constraint regarding the cardiovascular support index being at minimum 47 and at maximum 104 is captured by constraints 1 and 3, respectively.

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

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="milligrams_of_iron")
x2 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B9")

# Define the objective function
m.setObjective(6.68*x1 + 7.55*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.4*x1 + 0.14*x2 >= 47, "cardiovascular_support_index_min")
m.addConstr(-3*x1 + 4*x2 >= 0, "iron_vitamin_b9_ratio")
m.addConstr(0.4*x1 + 0.14*x2 <= 104, "cardiovascular_support_index_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of iron: {x1.x}")
    print(f"Milligrams of vitamin B9: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```