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

Let's denote:
- $x_1$ as the milligrams of iron,
- $x_2$ as the milligrams of vitamin C.

The objective function is to maximize: $3.43x_1 + 5.11x_2$

Given constraints:
1. Digestive support index for iron: $3x_1$
2. Cardiovascular support index for iron: $x_1$
3. Kidney support index for iron: $22x_1$
4. Digestive support index for vitamin C: $32x_2$
5. Cardiovascular support index for vitamin C: $2x_2$
6. Kidney support index for vitamin C: $16x_2$
7. Total digestive support index minimum: $3x_1 + 32x_2 \geq 73$
8. Total cardiovascular support index minimum: $x_1 + 2x_2 \geq 140$
9. Total kidney support index minimum: $22x_1 + 16x_2 \geq 58$
10. Linear constraint: $-2x_1 + 7x_2 \geq 0$
11. Total digestive support index maximum: $3x_1 + 32x_2 \leq 147$
12. Total cardiovascular support index maximum: $x_1 + 2x_2 \leq 213$
13. Total kidney support index maximum: $22x_1 + 16x_2 \leq 129$

Now, let's represent the problem symbolically:
```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',
    'x1 + 2*x2 >= 140',
    '22*x1 + 16*x2 >= 58',
    '-2*x1 + 7*x2 >= 0',
    '3*x1 + 32*x2 <= 147',
    'x1 + 2*x2 <= 213',
    '22*x1 + 16*x2 <= 129'
  ]
}
```

Next, we'll write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="milligrams_of_iron", lb=0)  # Non-negative, can be fractional
x2 = m.addVar(name="milligrams_of_vitamin_C", lb=0)  # Non-negative, can be fractional

# Set the objective function
m.setObjective(3.43*x1 + 5.11*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 32*x2 >= 73, name="digestive_support_min")
m.addConstr(x1 + 2*x2 >= 140, name="cardiovascular_support_min")
m.addConstr(22*x1 + 16*x2 >= 58, name="kidney_support_min")
m.addConstr(-2*x1 + 7*x2 >= 0, name="linear_constraint")
m.addConstr(3*x1 + 32*x2 <= 147, name="digestive_support_max")
m.addConstr(x1 + 2*x2 <= 213, name="cardiovascular_support_max")
m.addConstr(22*x1 + 16*x2 <= 129, name="kidney_support_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of iron: {x1.x}")
    print(f"Milligrams of vitamin C: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```