## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of iron' and 'milligrams of vitamin C'. Let's denote 'milligrams of iron' as $x_1$ and 'milligrams of vitamin C' as $x_2$. The objective function to maximize is $3.43x_1 + 5.11x_2$.

## 2: List the constraints in symbolic notation
The constraints given are:
- Digestive support index: $3x_1 + 32x_2 \geq 73$
- Cardiovascular support index: $1x_1 + 2x_2 \geq 140$
- Kidney support index: $22x_1 + 16x_2 \geq 58$
- Additional constraint: $-2x_1 + 7x_2 \geq 0$
- Upper bounds:
  - Digestive support index: $3x_1 + 32x_2 \leq 147$
  - Cardiovascular support index: $1x_1 + 2x_2 \leq 213$
  - Kidney support index: $22x_1 + 16x_2 \leq 129$

## 3: Define the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of iron'), ('x2', 'milligrams of vitamin C')],
'objective_function': '3.43*x1 + 5.11*x2',
'constraints': [
    '3*x1 + 32*x2 >= 73',
    '1*x1 + 2*x2 >= 140',
    '22*x1 + 16*x2 >= 58',
    '-2*x1 + 7*x2 >= 0',
    '3*x1 + 32*x2 <= 147',
    '1*x1 + 2*x2 <= 213',
    '22*x1 + 16*x2 <= 129'
]
}
```

## 4: Implement the problem in Gurobi
To solve this problem using Gurobi, we will use Python. First, ensure you have Gurobi installed in your environment.

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="milligrams_of_iron", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="milligrams_of_vitamin_C", lb=0)  # No lower bound specified, assuming 0

    # Objective function
    model.setObjective(3.43 * x1 + 5.11 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(3 * x1 + 32 * x2 >= 73, name="digestive_support_index_min")
    model.addConstr(1 * x1 + 2 * x2 >= 140, name="cardiovascular_support_index_min")
    model.addConstr(22 * x1 + 16 * x2 >= 58, name="kidney_support_index_min")
    model.addConstr(-2 * x1 + 7 * x2 >= 0, name="additional_constraint")
    model.addConstr(3 * x1 + 32 * x2 <= 147, name="digestive_support_index_max")
    model.addConstr(1 * x1 + 2 * x2 <= 213, name="cardiovascular_support_index_max")
    model.addConstr(22 * x1 + 16 * x2 <= 129, name="kidney_support_index_max")

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```