[ONNX] Update DequantizeLinear21 converter (openvinotoolkit#27351)

### Details:
Aligned with the canonical form of the dequantization subgraph.

Reshape op has been moved up right after the Constant, it will be const
folded in MOC, this is ok, Reshape const folding doesn't copy a
constant, just copies a pointer.

And ConvertLike were replaced with Convert. Perhaps that's a pretty
rough change and we need to add a check here that the scale is a
contant. And in that case use Convert instead of ConvertLike, if scale
is not a constant, maybe we should leave ConvertLike.

### Tickets:
 - *https://jira.devtools.intel.com/browse/CVS-156329*

src/frontends/onnx/frontend/src/op/dequantize_linear.cpp

@@ -221,36 +221,39 @@ ov::OutputVector dequantize_linear(const ov::frontend::onnx::Node& node) {
     FRONT_END_GENERAL_CHECK(src_x.get_partial_shape().is_static(),
                             "DequantizeLinear cannot operate with dynamic shapes of input X");
 
-    const auto& unsqueezed_axes = std::make_shared<v0::Constant>(ov::element::i64, Shape{1}, std::vector<int64_t>{1});
-
-    if (inputs.size() > 2) {
-        zp = inputs[2];
-        if (zp.get_element_type() != scale.get_element_type()) {
-            zp = std::make_shared<v1::ConvertLike>(zp, scale);
-        }
-        zp = std::make_shared<v0::Unsqueeze>(zp, unsqueezed_axes);
-    }
-
     const auto axis = node.get_attribute_value<int64_t>("axis", 1);
     const auto block_size = static_cast<size_t>(node.get_attribute_value<int64_t>("block_size", 0));
-    const auto scale_type = scale.get_element_type();
 
     FRONT_END_GENERAL_CHECK(axis == 0, "Axis != 0 isn't supported");
     FRONT_END_GENERAL_CHECK(block_size > 0, "block_size must be greater than zero");
     FRONT_END_GENERAL_CHECK(
         src_x.get_shape()[0] % block_size == 0,
         "DequantizeLinear doesn't support case when first dimension of X cannot be divided by block_size");
 
-    const auto& x = src_x.get_element_type() == scale_type ? src_x : std::make_shared<v1::ConvertLike>(src_x, scale);
+    ov::Output<ov::Node> broadcastable_x = op::util::reshape(
+        src_x,
+        Shape{static_cast<size_t>(src_x.get_shape()[0]) / block_size, block_size, src_x.get_shape()[1]});
+
+    const auto& unsqueezed_axes = std::make_shared<v0::Constant>(ov::element::i64, Shape{1}, std::vector<int64_t>{1});
+
+    const auto scale_type = scale.get_element_type();
+    if (inputs.size() > 2) {
+        zp = inputs[2];
+        if (zp.get_element_type() != scale.get_element_type()) {
+            zp = std::make_shared<v0::Convert>(zp, scale_type);
+        }
+        zp = std::make_shared<v0::Unsqueeze>(zp, unsqueezed_axes);
+    }
+
+    const auto& x = src_x.get_element_type() == scale_type ? broadcastable_x
+                                                           : std::make_shared<v0::Convert>(broadcastable_x, scale_type);
     // For further broadcasting scales and zp - reshape input to a shape [x.shape[0]/block_size, block_size, x.shape[1]]
-    ov::Output<ov::Node> broadcastable_x =
-        op::util::reshape(x, Shape{static_cast<size_t>(x.get_shape()[0]) / block_size, block_size, x.get_shape()[1]});
 
     // Adding additional dimension for broadcasting
     scale = std::make_shared<v0::Unsqueeze>(scale, unsqueezed_axes);
 
     if (zp.get_node_shared_ptr()) {
-        broadcastable_x = std::make_shared<v1::Subtract>(broadcastable_x, zp);
+        broadcastable_x = std::make_shared<v1::Subtract>(x, zp);
     }
 
     const auto& scaled_x = std::make_shared<v1::Multiply>(broadcastable_x, scale);