## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B1' and 'milligrams of vitamin A', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $5.56x_1 + 7.0x_2$.

## Step 3: Convert the constraints into symbolic notation
The constraints are:
1. $10x_1 \leq 117$ (cardiovascular support index of $x_1$ is 10, but this seems to be an upper bound on $x_1$ itself or a misunderstanding since $x_0$ and $x_1$ are given for $r0$),
2. $14x_2 \leq 117$ (similarly, for $x_2$),
3. $10x_1 + 14x_2 \geq 26$ (total combined index is at least 26),
4. $-7x_1 + 4x_2 \geq 0$ (given constraint),
5. $10x_1 + 14x_2 \leq 104$ (total combined index is no more than 104),
6. $x_1$ can be non-whole (implying $x_1$ is continuous),
7. $x_2$ must be a whole number (implying $x_2$ is integer).

Correcting based on the provided attributes:
- The cardiovascular support index for $x_1$ is given by $r0$ with $x0 = 10$ and for $x_2$ by $x1 = 14$. The upper bound for the index is 117.

## 4: Formulate the problem in a standard optimization format
Maximize $5.56x_1 + 7.0x_2$

Subject to:
- $10x_1 \leq 117$
- $14x_2 \leq 117$
- $10x_1 + 14x_2 \geq 26$
- $-7x_1 + 4x_2 \geq 0$
- $10x_1 + 14x_2 \leq 104$
- $x_2$ is an integer.

## 5: Provide the symbolic representation
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of vitamin A')],
'objective_function': '5.56*x1 + 7.0*x2',
'constraints': [
    '10*x1 <= 117',
    '14*x2 <= 117',
    '10*x1 + 14*x2 >= 26',
    '-7*x1 + 4*x2 >= 0',
    '10*x1 + 14*x2 <= 104'
]
}
```

## 6: Implement the problem using Gurobi
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # Assuming non-negative
    x2 = model.addVar(name="x2", lb=0, vtype=gurobi.GRB.INTEGER)  # Integer variable

    # Objective function
    model.setObjective(5.56 * x1 + 7.0 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(10 * x1 <= 117)
    model.addConstr(14 * x2 <= 117)
    model.addConstr(10 * x1 + 14 * x2 >= 26)
    model.addConstr(-7 * x1 + 4 * x2 >= 0)
    model.addConstr(10 * x1 + 14 * x2 <= 104)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```