To solve the given optimization problem, we first need to identify the decision variables, the objective function, and the constraints.

- Decision Variables:
  - `x0`: milligrams of calcium
  - `x1`: milligrams of vitamin B7`

- Objective Function: Minimize `5.78*x0 + 5.62*x1`

- Constraints:
  1. Kidney support index for milligrams of calcium is 13.
  2. Cognitive performance index of milligrams of calcium is 1.
  3. Muscle growth index of milligrams of calcium is 13.
  4. Kidney support index for milligrams of vitamin B7 is 11.
  5. Cognitive performance index of milligrams of vitamin B7 is 1.
  6. Muscle growth index of milligrams of vitamin B7 is 11.
  7. Total combined kidney support index from both sources is at least 44.
  8. Same as 7, emphasizing the minimum requirement.
  9. Total combined cognitive performance index from both sources is at least 15.
  10. Same as 9, reiterating the minimum requirement.
  11. Total combined muscle growth index from both sources is at least 13.
  12. Same as 11, emphasizing the minimum requirement.
  13. `4*x0 - 9*x1 >= 0`
  14. Total combined kidney support index from both sources is at most 98.
  15. Total combined cognitive performance index from both sources is at most 53.
  16. Total combined muscle growth index from both sources is at most 34.
  17. `x0` must be a whole number.
  18. `x1` can be any non-negative real number.

However, upon closer inspection, constraints 2, 3, 5, and 6 seem to describe attributes of the variables rather than constraints on the optimization problem itself. Similarly, constraints 7 through 12 and 14 through 16 describe limits on indices that are already defined by the attributes given for `x0` and `x1`. Thus, we'll focus on translating the actual constraints into Gurobi code.

```python
from gurobipy import *

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

# Decision Variables
x0 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_calcium")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B7", lb=0)

# Objective Function: Minimize 5.78*x0 + 5.62*x1
m.setObjective(5.78*x0 + 5.62*x1, GRB.MINIMIZE)

# Constraints
# Total combined kidney support index from both sources is at least 44
m.addConstr(13*x0 + 11*x1 >= 44, name="kidney_support_index_min")

# Total combined cognitive performance index from both sources is at least 15
m.addConstr(x0 + x1 >= 15, name="cognitive_performance_index_min")

# Total combined muscle growth index from both sources is at least 13
m.addConstr(13*x0 + 11*x1 >= 13, name="muscle_growth_index_min")

# 4*x0 - 9*x1 >= 0
m.addConstr(4*x0 - 9*x1 >= 0, name="custom_constraint")

# Total combined kidney support index from both sources is at most 98
m.addConstr(13*x0 + 11*x1 <= 98, name="kidney_support_index_max")

# Total combined cognitive performance index from both sources is at most 53
m.addConstr(x0 + x1 <= 53, name="cognitive_performance_index_max")

# Total combined muscle growth index from both sources is at most 34
m.addConstr(13*x0 + 11*x1 <= 34, name="muscle_growth_index_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"milligrams_of_calcium: {x0.x}")
    print(f"milligrams_of_vitamin_B7: {x1.x}")
else:
    print("No optimal solution found")
```