To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical formulation and then express this formulation in Gurobi's Python interface. The objective is to maximize the function `9.48 * x0 + 7.02 * x1`, where `x0` represents the milligrams of vitamin B1 and `x1` represents the milligrams of vitamin B12, subject to several constraints.

Given constraints:
- The cardiovascular support index for vitamin B1 is 5, and for vitamin B12 is 6.
- The total combined cardiovascular support index must be at least 38.
- The expression `-3 * x0 + 5 * x1` should be at least zero.
- The total combined cardiovascular support index should not exceed 82.

Let's denote `x0` as the amount of milligrams of vitamin B1 and `x1` as the amount of milligrams of vitamin B12. We are maximizing `9.48*x0 + 7.02*x1`.

### Constraints Translation:
1. `5*x0 + 6*x1 >= 38` (Total cardiovascular support index is at least 38)
2. `-3*x0 + 5*x1 >= 0` (Given constraint)
3. `5*x0 + 6*x1 <= 82` (Total cardiovascular support index should not exceed 82)

Since both `x0` and `x1` can be non-integer, we don't need to specify them as integers in the model.

Here is how you could implement this optimization problem using Gurobi's Python interface:

```python
from gurobipy import *

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

# Create variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B1")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B12")

# Set the objective
m.setObjective(9.48*x0 + 7.02*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x0 + 6*x1 >= 38, "cardiovascular_support_min")
m.addConstr(-3*x0 + 5*x1 >= 0, "given_constraint")
m.addConstr(5*x0 + 6*x1 <= 82, "cardiovascular_support_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B1: {x0.x}")
    print(f"Milligrams of Vitamin B12: {x1.x}")
    print(f"Objective value: {m.objVal}")
else:
    print("No optimal solution found")
```