To solve the optimization problem described, we need to translate the given objective function and constraints into a format that can be understood by Gurobi, a linear programming solver. The objective is to minimize the value of `6.46 * vitamin_D + 1.98 * calcium + 6.73 * vitamin_B2`, subject to various constraints related to cardiovascular support indices and muscle growth indices.

The constraints provided are as follows:
- Minimum and maximum combined cardiovascular support indices for different combinations of vitamins/minerals.
- Minimum combined muscle growth indices for different combinations of vitamins/minerals.
- The variables (vitamin D, calcium, vitamin B2) can be fractional, meaning they do not have to be integers.

Given these conditions, the problem can be formulated as a linear programming problem. Here's how we can represent it in Gurobi:

```python
from gurobipy import *

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

# Define variables - since they can be fractional, we use continuous type
vitamin_D = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_D")
calcium = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_calcium")
vitamin_B2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B2")

# Objective function: Minimize
m.setObjective(6.46 * vitamin_D + 1.98 * calcium + 6.73 * vitamin_B2, GRB.MINIMIZE)

# Constraints
# Minimum combined cardiovascular support indices
m.addConstr(23 * vitamin_D + 33 * vitamin_B2 >= 28, name="cardio_support_vitD_vitB2_min")
m.addConstr(23 * vitamin_D + 34 * calcium >= 51, name="cardio_support_vitD_calcium_min")
m.addConstr(23 * vitamin_D + 34 * calcium + 33 * vitamin_B2 >= 51, name="cardio_support_all_min")

# Maximum combined cardiovascular support index for vit D and vit B2
m.addConstr(23 * vitamin_D + 33 * vitamin_B2 <= 168, name="cardio_support_vitD_vitB2_max")

# Minimum combined muscle growth indices
m.addConstr(10 * vitamin_D + 9 * vitamin_B2 >= 23, name="muscle_growth_vitD_vitB2_min")
m.addConstr(10 * vitamin_D + 11 * calcium >= 20, name="muscle_growth_vitD_calcium_min")
m.addConstr(11 * calcium + 9 * vitamin_B2 >= 40, name="muscle_growth_calcium_vitB2_min")
m.addConstr(10 * vitamin_D + 11 * calcium + 9 * vitamin_B2 >= 46, name="muscle_growth_all_min")

# Optimize the model
m.optimize()

# Print results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Obj:", m.objVal)
```

This code defines a Gurobi model that minimizes the given objective function subject to the specified constraints. The variables `vitamin_D`, `calcium`, and `vitamin_B2` are defined as continuous, allowing for fractional values. After setting up the model, it is optimized, and the results (values of the variables and the objective function) are printed out.