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

Let's denote:
- $x_1$ as the quantity of milligrams of vitamin B1,
- $x_2$ as the quantity of milligrams of vitamin A.

The objective function to be maximized is given by $5.56x_1 + 7.0x_2$.

The constraints can be formulated as follows:
1. The cardiovascular support index for milligrams of vitamin B1 is 10: This translates directly into a constraint on the coefficient of $x_1$, but since it's not directly limiting $x_1$ without considering $x_2$, we'll incorporate this into the combined index constraints.
2. The cardiovascular support index for milligrams of vitamin A is 14: Similar to the first point, this will be part of the combined constraints.
3. The total combined cardiovascular support index from both vitamins should be at least 26: $10x_1 + 14x_2 \geq 26$.
4. $-7x_1 + 4x_2 \geq 0$.
5. The total combined cardiovascular support index must not exceed 104: $10x_1 + 14x_2 \leq 104$.
6. Since the problem mentions that the amount of milligrams of vitamin B1 can be non-whole but the amount of milligrams of vitamin A must be a whole number, we have $x_2 \in \mathbb{Z}$.

Thus, our symbolic representation is:
```json
{
  'sym_variables': [('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of vitamin A')],
  'objective_function': '5.56*x1 + 7.0*x2',
  'constraints': [
    '10*x1 + 14*x2 >= 26',
    '-7*x1 + 4*x2 >= 0',
    '10*x1 + 14*x2 <= 104'
  ]
}
```

Now, let's implement this problem in Gurobi:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B1")
x2 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_A")

# Set the objective function
m.setObjective(5.56*x1 + 7.0*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10*x1 + 14*x2 >= 26, "cardiovascular_support_index_min")
m.addConstr(-7*x1 + 4*x2 >= 0, "vitamin_ratio_constraint")
m.addConstr(10*x1 + 14*x2 <= 104, "cardiovascular_support_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B1: {x1.x}")
    print(f"Milligrams of Vitamin A: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```