To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of regular microphones to be stocked.
- $x_2$ as the number of premium microphones to be stocked.

The objective function aims to maximize profit. Given that the regular microphone yields a profit of $80 and the premium one yields a profit of $95, but considering the costs are subtracted from the selling prices to get these profits, we should directly use the given profits for calculation as they already account for the cost:

- Profit per regular microphone = $80 (since it costs $75 and sells for $155).
- Profit per premium microphone = $95 (since it costs $100 and sells for $195).

Thus, the objective function to maximize profit is: $80x_1 + 95x_2$.

The constraints are:
1. Budget constraint: The total cost of microphones should not exceed $30,000.
   - Cost per regular microphone = $75.
   - Cost per premium microphone = $100.
   - Thus, the budget constraint is: $75x_1 + 100x_2 \leq 30000$.

2. Demand constraint: The total number of microphones should not exceed 300.
   - Thus, the demand constraint is: $x_1 + x_2 \leq 300$.

3. Non-negativity constraints: The number of microphones cannot be negative.
   - Thus, we have: $x_1 \geq 0$ and $x_2 \geq 0$.

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'number of regular microphones'), ('x2', 'number of premium microphones')],
  'objective_function': '80*x1 + 95*x2',
  'constraints': ['75*x1 + 100*x2 <= 30000', 'x1 + x2 <= 300', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code to solve this linear programming problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="regular_microphones", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="premium_microphones", lb=0)

# Set the objective function
m.setObjective(80*x1 + 95*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(75*x1 + 100*x2 <= 30000, "budget_constraint")
m.addConstr(x1 + x2 <= 300, "demand_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of regular microphones: {x1.x}")
    print(f"Number of premium microphones: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```