To solve this optimization problem, we first need to translate the given natural language description into a symbolic representation. This involves defining variables for each of the items mentioned (milligrams of potassium, milligrams of vitamin D, and milligrams of vitamin B1), formulating the objective function using these variables, and then listing out all the constraints in terms of these variables.

Let's define our variables as follows:
- \(x_1\): milligrams of potassium
- \(x_2\): milligrams of vitamin D
- \(x_3\): milligrams of vitamin B1

The objective function to minimize is given by:
\[5.1x_1 + 6.54x_2 + 7.3x_3\]

Now, let's list the constraints based on the description provided:
1. The digestive support index for each component does not directly form a constraint but indicates how each contributes to the total digestive support index.
2. The total combined digestive support index from milligrams of potassium and milligrams of vitamin D must be at least 20: \(16x_1 + 5x_2 \geq 20\)
3. The total combined digestive support index from all three (milligrams of potassium, milligrams of vitamin D, and milligrams of vitamin B1) must be at least 20: \(16x_1 + 5x_2 + 8x_3 \geq 20\)
4. \(2x_2 - 3x_3 \geq 0\)
5. \(7x_1 - 5x_2 \geq 0\)

Since the problem allows for fractional numbers of milligrams for all vitamins and potassium, we do not need to specify integer constraints.

The symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'milligrams of potassium'), ('x2', 'milligrams of vitamin D'), ('x3', 'milligrams of vitamin B1')],
    'objective_function': '5.1*x1 + 6.54*x2 + 7.3*x3',
    'constraints': [
        '16*x1 + 5*x2 >= 20',
        '16*x1 + 5*x2 + 8*x3 >= 20',
        '2*x2 - 3*x3 >= 0',
        '7*x1 - 5*x2 >= 0'
    ]
}
```

Here is the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = model.addVar(name="milligrams_of_potassium")
x2 = model.addVar(name="milligrams_of_vitamin_D")
x3 = model.addVar(name="milligrams_of_vitamin_B1")

# Set the objective function
model.setObjective(5.1*x1 + 6.54*x2 + 7.3*x3, GRB.MINIMIZE)

# Add constraints
model.addConstr(16*x1 + 5*x2 >= 20, name="digestive_support_index_12")
model.addConstr(16*x1 + 5*x2 + 8*x3 >= 20, name="digestive_support_index_123")
model.addConstr(2*x2 - 3*x3 >= 0, name="vitamin_constraint")
model.addConstr(7*x1 - 5*x2 >= 0, name="potassium_vitamin_constraint")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of potassium: {x1.x}")
    print(f"Milligrams of vitamin D: {x2.x}")
    print(f"Milligrams of vitamin B1: {x3.x}")
else:
    print("No optimal solution found.")
```