## Problem Description and Formulation

The problem is an optimization problem where we need to minimize the objective function:

`6.68 * milligrams of iron + 7.55 * milligrams of vitamin B9`

subject to several constraints.

### Constraints

1. The cardiovascular support index of milligrams of iron is 0.4.
2. The cardiovascular support index for milligrams of vitamin B9 is 0.14.
3. The total combined cardiovascular support index from milligrams of iron plus milligrams of vitamin B9 must be 47 at minimum.
4. The total combined cardiovascular support index from milligrams of iron, and milligrams of vitamin B9 should be no less than 47 (redundant with constraint 3).
5. `-3 * milligrams of iron + 4 * milligrams of vitamin B9 >= 0`.
6. The total combined cardiovascular support index from milligrams of iron plus milligrams of vitamin B9 must be at maximum 104.

### Gurobi Code Formulation

We will use Gurobi's Python API to formulate and solve this problem.

```python
import gurobi

# Create a new Gurobi model
model = gurobi.Model()

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

# Objective function
model.setObjective(6.68 * milligrams_of_iron + 7.55 * milligrams_of_vitamin_B9, gurobi.GRB.MINIMIZE)

# Constraints
r0_x0 = 0.4
r0_x1 = 0.14
model.addConstr(r0_x0 * milligrams_of_iron + r0_x1 * milligrams_of_vitamin_B9 >= 47, name="cardiovascular_support_index_min")
model.addConstr(r0_x0 * milligrams_of_iron + r0_x1 * milligrams_of_vitamin_B9 <= 104, name="cardiovascular_support_index_max")
model.addConstr(-3 * milligrams_of_iron + 4 * milligrams_of_vitamin_B9 >= 0, name="linear_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of iron: {milligrams_of_iron.varValue}")
    print(f"Milligrams of vitamin B9: {milligrams_of_vitamin_B9.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```