## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin A' and 'milligrams of vitamin B5'. Let's denote 'milligrams of vitamin A' as $x_1$ and 'milligrams of vitamin B5' as $x_2$. The objective function to minimize is $2.09x_1 + 2.11x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- Cognitive performance index for $x_1$ is 7, and for $x_2$ is 22.
- Kidney support index for $x_1$ is 5, and for $x_2$ is 26.
- Cardiovascular support index for $x_1$ is 16, and for $x_2$ is 26.
- The total combined cognitive performance index must be 13 or more: $7x_1 + 22x_2 \geq 13$.
- The total combined kidney support index must be 45 or more: $5x_1 + 26x_2 \geq 45$.
- The total combined cardiovascular support index must be 37 or more: $16x_1 + 26x_2 \geq 37$.
- $-7x_1 + 3x_2 \geq 0$.
- The total combined cognitive performance index must be 58 or less: $7x_1 + 22x_2 \leq 58$.
- The total combined kidney support index must be 133 or less: $5x_1 + 26x_2 \leq 133$.
- The total combined cardiovascular support index must be 61 or less: $16x_1 + 26x_2 \leq 61$.
- $x_1$ must be an integer.
- $x_2$ is a continuous variable.

## 3: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin A'), ('x2', 'milligrams of vitamin B5')],
'objective_function': '2.09*x1 + 2.11*x2',
'constraints': [
    '7*x1 + 22*x2 >= 13',
    '5*x1 + 26*x2 >= 45',
    '16*x1 + 26*x2 >= 37',
    '-7*x1 + 3*x2 >= 0',
    '7*x1 + 22*x2 <= 58',
    '5*x1 + 26*x2 <= 133',
    '16*x1 + 26*x2 <= 61',
    'x1 >= 0', 'x1 <= 70', 
    'x2 >= 0'
]
}
```

## 4: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_A", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="milligrams_of_vitamin_B5")

    # Objective function
    model.setObjective(2.09 * x1 + 2.11 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(7 * x1 + 22 * x2 >= 13, name="cognitive_performance_index")
    model.addConstr(5 * x1 + 26 * x2 >= 45, name="kidney_support_index")
    model.addConstr(16 * x1 + 26 * x2 >= 37, name="cardiovascular_support_index")
    model.addConstr(-7 * x1 + 3 * x2 >= 0, name="vitamin_interaction")
    model.addConstr(7 * x1 + 22 * x2 <= 58, name="max_cognitive_performance_index")
    model.addConstr(5 * x1 + 26 * x2 <= 133, name="max_kidney_support_index")
    model.addConstr(16 * x1 + 26 * x2 <= 61, name="max_cardiovascular_support_index")

    # Bounds
    model.addConstr(x1 >= 0, name="non_negative_x1")
    model.addConstr(x1 <= 70, name="max_x1")
    model.addConstr(x2 >= 0, name="non_negative_x2")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin A: {x1.varValue}")
        print(f"Milligrams of vitamin B5: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```